Systems and methods for imaging of an anatomical structure

ABSTRACT

Systems and method for optical imaging of an animal include a body conforming animal mold, which is shaped and sized to hold an animal in an immobilized and geometrically defined position and a gantry, which can include multiple optical mirrors to provide for simultaneous imaging of multiple different views of an animal within a body conforming animal mold.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are hereby incorporated by reference under 37 CFR 1.57. Thisapplication is a continuation of U.S. application Ser. No. 15/621,983filed on Jun. 13, 2017, which claims the benefit of U.S. ProvisionalApplication No. 62/350,129 filed on Jun. 14, 2016, U.S. ProvisionalApplication No. 62/350,128 filed on Jun. 14, 2016, U.S. ProvisionalApplication No. 62/382,654 filed on Sep. 1, 2016, and U.S. ProvisionalApplication No. 62/382,679 filed on Sep. 1, 2016, each of which ishereby incorporated by reference in its entirety.

BACKGROUND Field

The present disclosure relates to imaging methods and apparatuses, andmore particularly, in some arrangements, relates to methods andapparatus for imaging of an anatomical structure.

Description of the Related Art

Preclinical imaging is a modality for studying diseases and pathologicaldysfunction in small animal models. Preclinical imaging can allow forresearch into human diseases using animal models, such as rodents, andcan further be used to develop new therapeutics. Traditional preclinicalimaging data, such as that produced by two dimensional (“2D”)bioluminescence imaging (“BLI”) or three dimensional (“3D”)bioluminescence tomography (“BLT”), is prone to large variability ininterpretation due to user-subjectivity in image data processing, e.g.,manual selection of regions-of-interest, variability in animal pose andsurface rendering, ad-hoc tomographic reconstruction parameters orunknown image noise.

There is a need to address reproducibility in preclinical studies fortranslational research given increasing drug development costs and highclinical failure rates. BLI is, however, mostly limited to planarimaging of light intensities at the surface of the animal, where lightintensity emitted from within has been attenuated by the thickness oftissue it traversed. Due to depth-dependence of surficial lightintensity, BLI cannot provide any quantitative information originatingfrom a deep-seated target inside a mouse model such as the actual amountof luminescent bacteria, tumor cells, or stem cells growing or migratinginside the animal.

Region- or volume-of-interest analysis can be used for quantifyingbacterial burden, proliferation, and therapeutic efficacy. However, BLIreliance on the operator-dependent visual inspection of aregion-of-interest (ROI) for drawing conclusions about study outcome canbe difficult to reproduce by an independent external review. Imageanalysis of large-scale data sets and cross-examination between imagesof different animals and time points using different mathematical toolscan also be prohibited due to a number of procedural and computationalconstraints such as the planar surface images without depth information;differences in individual animal pose, animal size, and camera view; andthe limited local computational resources. Furthermore, the automaticco-registration of optical images to an accurate anatomical map iscompletely missing without the use of an additional imaging modalitylike CT or MRI with some elaborate and/or resource intensiveco-registration.

Bacterial infections are an exemplary case for demonstrating limitationsof BLI in preclinical research and drug development. Bacterialinfections impose a costly health burden worldwide which is compoundedby the alarming increase of multi-drug resistant (MDR) Gram-negativebacteria, and many of these infections are in the urogenital tract.Urogenital tract infections (UTI) are typically caused by aGram-negative E. coli (75-90%) that afflicts more than 250 millionadults and children worldwide each year. According to the CDC, 75% ofhospital acquired UTIs were associated with the placement of a urinarycatheter where 15-25% of all hospitalized patients receive a urinarycatheter. However, pharmaceutical companies tasked with developing thenext class of antibiotics and new antimicrobial catheters do not havethe tools to monitor bacterial infections quantitatively in real-time inanimal models of infection. Thus many novel antibiotics are stalled atthe preclinical stage. Compounding this public health problem is thatthe number of novel antibiotics has been on the decline over the pastdecade. An essential step in the development of novel antibiotics tocombat urinary MDR E. coli is done in small animal models withbioluminescent bacteria.

However, the state of the art optical imaging systems can only outputqualitative data with limited data points and, thus, cannot assess theefficacy in vivo with quantitative information. Therefore, there is anurgent unmet need for in vivo monitoring of the spatial and temporaldynamics of bacterial organ burden in response to therapeuticintervention.

Current BLI methods are based on the assumption that the measured lightintensity at the tissue surface (photons s-1cm-2) directly correlates tothe colony forming units (CFU) of bacteria inside an organ. However,light is strongly attenuated by tissue and the measured signal may bedependent on: (i) the unknown spatial location of bacteria; (ii) theheterogeneous optical tissue properties; and (iii) the animal's size,pose, and shape. Bioluminescence tomography uses models of lightattenuation in tissue from bioluminescent source to the animal surfaceto correct for this decrease in brightness, which can be orders ofmagnitude. Commercially available bioluminescence tomography softwareassumes homogeneous tissue properties and a diffusion model of photonpropagation. The commercial software analysis requires tedious,subjective user-input for multiple steps of the tomographic process thatgive rise to inconsistent reconstructions with the same mouse image dataset. Commercial 3D bioluminescence reconstructions may therefore beprone to variability in reconstruction intensity and reconstructiondistribution width, due to user-subjectivity in image data processing.Furthermore, after optical images have been acquired, the investigatorvisually inspects the optical image of each individual animal andmanually selects a regions-of-interest (ROI) and determines some measureof the photon count within the ROI. This process is not only subject tointerpretation of results depending on the investigator's visualobservation but also on the animal's pose and size. Additionally, imageand reconstruction noise properties are not established inbioluminescence tomography, rendering regions-/volumes-of-interestanalysis prone to bias and error. Longitudinal study evaluation witherror-prone reconstructions further reduces the study robustness andreliability. Moreover, an anatomical reference that determines theactual site of infection inside the animal is completely missing.

Considering the current limitations of BLI for spatio-temporal imaging,automated and quantitative analysis of imaging results across differentanimals and study points has not been feasible yet.

Furthermore, cross-comparison of 2D and 3D image data for differentstudy time points or between different animals can be cumbersome basedon variability in the sizes and poses of animals and because imagepixels may not be consistently co-aligned with an animal's tissuesurface.

U.S. Patent Publication No. 2015/0012224, which is hereby incorporatedby reference herein in its entirety, discloses an optically transparentbody-shape-conforming animal mold, which can constrain an animal modelto a defined spatial position while the animal model is imaged. Asdescribed in U.S. Patent Publication No. 2015/0012224, when used withmice, a digital mouse atlas can provide an anatomical reference to theimaged animal and the animal mold can facilitate spatially aligning theimaged animal and the mouse atlas. The information from the imagedanimal and the mouse atlas can then be used to provide quantitativeinformation regarding the imaged mouse. While the methods and systems ofU.S. Patent Publication No. 2015/0012224 are useful for BLI and/or BLT,there is a general desire to improve the accuracy and ease of use of themethods and systems described therein.

SUMMARY

The systems, methods, and devices of the disclosure each have severalinnovative aspects, no single one of which is solely responsible for thedesirable attributes disclosed herein.

In one innovative aspect, an apparatus for housing an animal for opticalimaging is provided. The apparatus can include a top section configuredto encapsulate at least part of an animal, the mold piece including oneor more hinge connections; and a bottom section configured toencapsulate at least part of an animal, the bottom section including oneor more hinge connections configured to engage the one or more hingeconnections of the top section, wherein when the one or more hingeconnections of the top section connect with the one or more hingeconnections of the bottom section, the top section and bottom sectiondefine an inner cavity configured to encapsulate the animal, and one ormore openings configured to receive a gas from a gas supply, the one ormore openings being positioned so that gas from the gas supply canpermeate the inner cavity.

In another innovative aspect, an apparatus for housing an animal foroptical imaging is provided. The apparatus can include a top sectionconfigured to encapsulate at least part of an animal, the top sectionincluding a first end and a second end and a bottom section configuredto encapsulate at least part of an animal, the bottom section includinga first end and a second end, wherein the first end of the top sectionis movably secured to the first end of the bottom section, such that thetop section and bottom section are movable between an open position anda closed position, and wherein the second end of the top section and thesecond end of the bottom section are configured to detachably engage oneanother, wherein when the second end of the first mold piece and thesecond end of the bottom section are detachably engaged, the top sectionand bottom section define an inner cavity configured to encapsulate theanimal.

In a further innovative aspect, an assembly for optical imaging of aplurality of animals is provided. The assembly can include a holderconfigured to secure a plurality of molds, each mold configured toencapsulate an animal and a docking interface configured to engage theholder, wherein the docking interface is further configured to allow for180° rotation.

In a further innovative aspect, a method for collecting optical imagingdata for an animal is provided. The method can include placing an animalinto an interior chamber of a body conforming animal mold, supplying ananesthetic from a gas supply to the interior chamber, and performingoptical imaging of the animal in the mold.

In a further innovative aspect, an assembly for optical imaging of ananimal is provided. The assembly can include a plurality of opticalmirrors and a channel configured to receive an animal mold configured toencapsulate an animal. The animal mold can include a top section, abottom section, and a hinge including a pivot axis, wherein the topsection and the bottom section are configured to rotate about the pivotaxis between a closed position and an open position.

In a further innovative aspect, an assembly for optical imaging of aplurality of animals is provided. The assembly can include a pluralityof molds, each mold configured to encapsulate an animal, a holderconfigured to secure the plurality of molds; and a gas supply incommunication with the plurality of molds, wherein the gas supply isconfigured to direct gas to the plurality of molds.

In a further innovative aspect, a method for collecting optical imagingdata for an animal is provided. The method can include placing an animalinto an optical imaging assembly, performing optical imaging of theanimal, detecting a computer readable label in an optical image capturedby the optical imaging assembly, and extracting data from the computerreadable label.

In a further innovative aspect, a system for providing reproducibleimaging results indicative of an in vivo experimental result isprovided, the imaging results provided for presentation via a displayunit. The system includes an image receiver configured to receive firstimage data and second image data. The system includes a data storeincluding position definitions, wherein a first position definitionidentifies, for an animal subject imaged at a first time while in afirst position, a first location for an anatomical feature at the firsttime. A second position definition identifies, for an animal subjectimaged at a second time while in a second position, a second locationfor the anatomical feature at the second time. The system also includesa position detector configured to identify the first position definitionfor processing the first image data based on the first image data, andto identify second position definition for processing the second imagedata, based on the second image data. The system also includes an imageprocessor. The image processor is configured to receive a processingprotocol identifying a comparison for image data and an associatedresult based thereon. The comparison indicates one or more locations ofinput image data to compare and how to compare the indicated input imagedata. The associated result indicates an output imaging result toprovide for a comparison result. The image processor is also configuredto extract a portion of the first image data from the first location ofthe first image data and to extract a portion of the second image datafrom the second location of the second image data. The image processoris further configured to generate comparison data according to thecomparison identified in the processing protocol using image data at theone or more locations identified by the comparison from the firstportion of the first image data and the second portion of the secondimage data. The image processor is also configured to generate animaging result according to the processing protocol using the comparisondata and cause presentation of the imaging result via the display unit.

In some implementations of the system, the image receiver may beconfigured to receive the first image data from a first sensing device.In some implementations of the system, the image receiver may beconfigured to receive the second image data from a second sensingdevice.

The animal subject imaged by the system at the first time may an animaltest subject, and the animal subject imaged at the second time is theanimal test subject. That is, the system may image same animal subjectat the first and second time. In some implementations, the system mayimage different animal subjects at the first time and the second time.

In some implementations of the system the first location may identifyone or more pixel locations for the anatomical feature shown in thefirst image data. In such implementations, the second location mayidentify one or more pixel locations for the anatomical feature shown inthe second image data. The one or more locations of input image dataindicated by the comparison may include one or more pixel locations. Inimplementations where the location data identifies pixels, the imageprocessor may be configured to generate the imaging result by comparing,using the processing protocol, first pixel values at the one or morepixel locations of the anatomical feature shown in the first image datawith second pixel values at the one or more pixel locations of theanatomical feature shown in the second image data.

In some implementations, the first location may identify one or morevoxel locations for the anatomical feature shown in the first image dataand the second location may identify one or more voxel locations for theanatomical feature shown in the second image data. In suchimplementations, the one or more locations of input image data indicatedby the comparison includes one or more voxel locations. Inimplementations where the location data identifies voxels, the imageprocessor may be configured to generate the imaging result by comparing,using the processing protocol, first voxel values at the one or morevoxel locations of the anatomical feature shown in the first image datawith second voxel values at the one or more voxel locations of theanatomical feature shown in the second image data.

In some implementations, the position detector may be configured toidentify the first position by detecting, within the first image data,an identifiable mark associated with the first position. Theidentifiable mark may be identified on a mold in which the animalsubject was placed to capture the first image data. In suchimplementations, at least a portion of the mold is shown in the firstimage data.

An imaging controller may be included in some implementations of thesystem. The imaging controller may be configured to receive, from animaging device, information at a third time identifying the animalsubject to be imaged. The imaging controller may be further configuredto identify a third position definition for the animal subject, thethird position definition identifying, for the animal subject imagedwhile in a third position, a third location for the anatomical feature.The imaging controller may further generate a configuration commandindicating sensor parameters for imaging the animal subject using thethird position definition and the processing protocol and, in someimplementations, transmit the configuration command to the imagingdevice. The imaging controller may be further configured to generate theconfiguration command using imaging results for the animal subjectstored before the third time.

In a further innovative aspect, an image processing system is provided.The image processing system includes an image receiver configured toreceive image data from an imaging device. The image processing systemincludes a data store including a plurality of position definitions. Aposition definition identifies, for a given subject imaged while in aposition, a location for an anatomical feature of the subject. The imageprocessing system includes a position detector configured to identify,using the image data received from the imaging device, the positiondefinition for the image data. The image processing system furtherincludes an image processor configured to generate an imaging resultusing a portion of the image data at the location for the anatomicalfeature identified by the position definition.

In some implementations of the image processing system, the firstlocation may identify one or more pixel locations for the anatomicalfeature shown in the first image data and the second location identifiesone or more pixel locations for the anatomical feature shown in thesecond image data. In such implementations, the one or more locations ofinput image data indicated by the comparison include one or more pixellocations. Where the location data is identified using pixels, the imageprocessor may be configured to generate the imaging result by comparing,using the processing protocol, first pixel values at the one or morepixel locations of the anatomical feature shown in the first image datawith second pixel values at the one or more pixel locations of theanatomical feature shown in the second image data.

In some implementations of the image processing system, the firstlocation may identify one or more voxel locations for the anatomicalfeature shown in the first image data and the second location identifiesone or more voxel locations for the anatomical feature shown in thesecond image data. In such implementations, the one or more locations ofinput image data indicated by the comparison include one or more voxellocations. Where the location data is identified using voxels, the imageprocessor may be configured to generate the imaging result by comparing,using the processing protocol, first voxel values at the one or morevoxel locations of the anatomical feature shown in the first image datawith second voxel values at the one or more voxel locations of theanatomical feature shown in the second image data.

The position detector included in some implementations of the imageprocessing system may be configured to identify the first position bydetecting, within the first image data, an identifiable mark associatedwith the first position. For example, the identifiable mark may beidentified on a positioning assembly in which the subject was placed tocapture the first image data, at least a portion of the positioningassembly being shown in the first image data.

In a further innovative aspect, a computer implemented method isprovided. The method includes receiving position definitions, wherein aposition definition identifies, for a given subject imaged while in aposition, a location for an anatomical feature of the subject. Themethod also includes receiving image data from an imaging device. Themethod further includes identifying, using the image data received fromthe imaging device, the position definition for the image data. Themethod also includes generating an imaging result using a portion of theimage data at the location for the anatomical feature identified by theposition definition.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of various inventive features will now be described withreference to the following drawings. Throughout the drawings, referencenumbers may be re-used to indicate correspondence between referencedelements. The drawings are provided to illustrate example embodimentsdescribed herein and are not intended to limit the scope of thedisclosure.

FIG. 1A depicts a schematic view of an animal mold in an open positionin accordance with an illustrative embodiment.

FIG. 1B depicts a perspective view of an animal mold in an open positionin accordance with an illustrative embodiment.

FIG. 2 depicts a perspective view of the animal mold of FIG. 1B in aclosed position in accordance with an illustrative embodiment.

FIG. 3 depicts a side view the animal mold of FIG. 1B in a closedposition in accordance with an illustrative embodiment.

FIG. 4 depicts a top view of the animal mold of FIG. 1B in a closedposition in accordance with an illustrative embodiment.

FIG. 5 depicts a bottom view of the animal mold of FIG. 1B in a closedposition in accordance with an illustrative embodiment.

FIG. 6 depicts a rear view of the animal mold of FIG. 1B in a closedposition in accordance with an illustrative embodiment.

FIG. 7 depicts a front view of the animal mold of FIG. 1B a closedposition in accordance with an illustrative embodiment.

FIG. 8 depicts a cross sectional view of the animal mold of FIG. 1B in aclosed position in accordance with an illustrative embodiment.

FIG. 9 depicts a bottom view of a top section of the animal mold of FIG.1B mold in accordance with an illustrative embodiment.

FIG. 10 depicts a top view of a bottom section of the animal mold ofFIG. 1B in accordance with an illustrative embodiment.

FIG. 11A depicts a schematic view of a gantry in accordance with anillustrative embodiment.

FIG. 11B depicts a perspective view of a gantry in a closed position inaccordance with an illustrative embodiment.

FIG. 12 depicts a perspective view of the gantry of FIG. 11B in an openposition in accordance with an illustrative embodiment.

FIG. 13 depicts a top view of the gantry of FIG. 11B in a closedposition in accordance with an illustrative embodiment.

FIG. 14 depicts a bottom view of the gantry of FIG. 11B in a closedposition in accordance with an illustrative embodiment.

FIG. 15 depicts a top view of the gantry of FIG. 11B having severalcomponents removed in accordance with an illustrative embodiment.

FIG. 16 shows a side view of the gantry of FIG. 11B having severalcomponents removed in accordance with an illustrative embodiment.

FIG. 17 shows a perspective view of the gantry of FIG. 11B havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 18 depicts a perspective view of the gantry of FIG. 11B havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 19 depicts a perspective view of the gantry of FIG. 11B a closedposition in accordance with an illustrative embodiment.

FIG. 20 depicts a perspective view of an imaging gantry in accordancewith an illustrative embodiment.

FIG. 21 depicts a perspective view of a docking interface of the imaginggantry in accordance of FIG. 20 with an illustrative embodiment.

FIG. 22 depicts a top view of a holder of the imaging gantry of FIG. 20in accordance with an illustrative embodiment of the present disclosureshowing several internal components.

FIG. 23 depicts a perspective view of a holder of the imaging gantry ofFIG. 20 connected to a gas supply in accordance with an illustrativeembodiment.

FIG. 24 depicts a perspective view of an animal mold in a closedposition in accordance with an illustrative embodiment.

FIG. 25 depicts a perspective view of the animal mold of FIG. 24 inaccordance with an illustrative embodiment.

FIG. 26 depicts a cross-sectional view of the animal mold of FIG. 24 inaccordance with an illustrative embodiment.

FIG. 27 depicts a front view of the animal mold of FIG. 24 in accordancewith an illustrative embodiment.

FIG. 28 depicts a rear view of the animal mold of FIG. 24 in accordancewith an illustrative embodiment.

FIG. 29 depicts a bottom view of a top section of the animal mold ofFIG. 24 in accordance with an illustrative embodiment.

FIG. 30 depicts a top view of a bottom section of the animal mold ofFIG. 24 in accordance with an illustrative embodiment.

FIG. 31 depicts a perspective view of a gantry in a closed position inaccordance with an illustrative embodiment.

FIG. 32 depicts a top view of the gantry of FIG. 31 having severalcomponents removed in accordance with an illustrative embodiment.

FIG. 33A depicts a side view of the gantry of FIG. 31 having severalcomponents removed in accordance with an illustrative embodiment.

FIG. 33B depicts a schematic side view of the gantry of FIG. 31 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 34 depicts a perspective view of the gantry of FIG. 31 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 35 depicts a perspective view of the gantry of FIG. 31 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 36 depicts a perspective view of the gantry of FIG. 31 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 37 depicts a perspective view of the gantry of FIG. 31 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 38 depicts a perspective view of an alternative embodiment of thegantry of FIG. 31 having several components removed in accordance withan illustrative embodiment.

FIG. 39 depicts a perspective view of a gantry in a closed position inaccordance with an illustrative embodiment.

FIG. 40 depicts a perspective view of the gantry of FIG. 40 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 41 depicts a perspective view of the gantry of FIG. 40 havingseveral components removed in accordance with an illustrativeembodiment.

FIG. 42 depicts a flowchart of a process for collecting optical imagingdata of an animal in accordance with an illustrative embodiment.

FIG. 43 shows a system diagram showing several components that may beincluded in a cross-correlation imaging system.

FIG. 44 shows a functional block diagram showing an example imageprocessing server.

FIG. 45 shows a message flow diagram for an example position based imageprocessing.

FIG. 46 shows a process flow diagram for an example method of generatingan imaging result.

FIG. 47 shows an example of an analysis grid that may be included aprocessing protocol.

FIG. 48 shows an example of an image of a subject that may be includedin the image data.

FIG. 49 shows an overlay diagram for the example image of FIG. 48overlaid with the analysis grid of FIG. 47.

FIG. 50 shows a process flow diagram for an example method of generatinga cross-correlated imaging result.

FIG. 51A shows a graphic diagram of an example presentation ofcross-correlated imaging result.

FIG. 51B shows a graphic diagram of another example presentation of across-correlated imaging result.

FIG. 52 shows a plot diagram of a further example presentation of across-correlated imaging result.

FIG. 53 shows a message flow diagram for an example process ofdynamically configuring an imaging device.

FIG. 54 shows a process flow diagram for another example method ofgenerating a cross-correlated imaging result.

DETAILED DESCRIPTION

The present disclosure includes systems and methods for imaging of ananatomical structure such as an animal and in some embodiments a smallanimal such as a mouse. The systems and methods of some embodiments areparticularly useful for optical imaging such as, for example,bioluminescence imaging (“BLI”) or three dimensional (“3D”)bioluminescence tomography (“BLT”). However, some features andadvantages of embodiments disclosed herein may also have utility innon-optical imaging such as, for example, computed tomography (“CT”)scans, positron emission tomography (“PET”), single photon emissioncounting tomography (“SPECT”), or magnetic resonance imaging (“MRI”).

Some aspects of the present disclosure include an animal mold, which canhave a shape and size to hold an animal in an immobilized andgeometrically defined position. The animal mold can be made of a solidmaterial that is optically transparent or at least partially transparentto facilitate optical imaging of an animal within the interior of theanimal mold. In non-optical imaging embodiments, the mold can be mode ofa material that is transparent to the imaging modality and does notcause any signal distortions. The animal mold can be made from a varietyof techniques such as thermo vacuum molding, injection molding, and 3Dstereo lithography. Accordingly, while the term “mold” can sometimesimply something that is formed in a mold or formed from molten state,the term as used in herein is not limited to such a definition or such atechnique for forming the animal mold. In some arrangements, the animalmold can be configured so that the mold slightly compresses an animal inthe interior of the mold so that the animal is at least partiallyrestrained and so that the outer surface of the animal body can contactthe interior surface of the animal mold. In some arrangements, theanimal mold can provide a constant spatial frame of reference acrossdifferent animals, and can also provide a consistent surface for cameradetection points. Thus, in some embodiments, the body of the animal moldcan provide infrastructure for software analysis of animal data takingadvantage of a mutual three dimensional grid between data sets recordedusing the body of the animal mold. The mutual spatial coordinate frameprovided by the animal mold can further allow for cross-comparison ofdifferent preclinical imaging modalities including BLI, BLT,fluorescence, PET, SPECT, MRI, and CT in some arrangements. The animalmold can also be used in the creation of an organ probability map, astatistical representation of the average spatial organ distribution ofa given pool of animals with the same body weight while taking thebiological variation across different animals into account. The bodyconforming animal molds can come in a variety of sizes for differentweights, sizes, sexes and strains of animals. The mutual spatialcoordinate frame provided by the animal mold, as well as an organprobability map, can allow for analysis of imaging data across animalsof different weights, sizes, sexes and strains.

In some embodiments, optical imaging is performed on an animal moldusing a gantry. A gantry can include multiple optical mirrors to providefor simultaneous imaging of multiple different views of an animal withinan animal mold.

In some embodiments, optical imaging is performed simultaneously on aplurality of animal molds. A plurality of animal molds can be secured toa single holder for optical imaging. In some embodiments, the holder canbe configured to rotate about an axis to allow for imaging of differentviews of an animal within an animal mold. In some embodiments, theholder may be a gantry.

Some of the features described allow cross-correlation of image data. Ina laboratory setting, experimental data may be obtained in the form ofimages of a subject. Many experiments are performed on living subjectswhom are likely to move and grow over time. As discussed above, thispresents a challenge in performing quantitative, reproducibleexperiments using such image data. Furthermore, image data may becollected from different sensors (e.g., modalities). For example, aprotocol may call for BLI and MRI data collection. Cross-correlating thedata collected across modalities introduces a further challenged whenperforming quantitative, reproducible experiments. The image data mayinclude non-optical image data such as positron emission tomography(PET), magnetic resonance imaging (MRI), single-photon emission computedtomography (SPECT), or other nuclear imaging image data.

The cross-correlation features described may be implemented in an imageprocessing device or system. One example implementation may include ahardware plugin unit for bioluminescence imaging, a cloud-based serverusing image data from the hardware plug in unit for image reconstructionand analysis, and a client-based data communication and pre-processingtool. Some implementations may run in a distributed environment (e.g.,cloud computing service) for resource intensive computation tasks andautomated analysis of large data sets. In an automated image dataprocessing pipeline, the system may be configured to determine imagenoise and 3D reconstruction noise prior to receiving image data foranalysis using clustering algorithms blinded to cohorts such as controland treatment arms.

Such implementations may afford non-limiting advantages such asincreasing the productivity, transparency, and robustness ofpre-clinical research and drug development and accelerating scientificdiscoveries by providing a robust, distributed, and dynamicallyconfigured image processing resources. By providing operator-friendlyaccess to image processing resources and an automated workflow-basedinterface, investigators may carry out, speed up, and standardize manychallenging image analysis and reconstruction tasks that are currentlyimpossible or impractical due to the limitations of the existing BLItechnology and/or the local computer hardware (e.g. within the lab, onthe imaging device). Distributed computing services can be configuredfor high reliability, flexible scalability, and efficient allocation ofa large pool of resources (e.g., memory, power, processor time, networkbandwidth, etc.). In some implementations, the system may be accessed asa subscription service by users. The distributed service may bedynamically configured to process and cross-correlate images in avariety of formats, from a variety of imaging devices, and for a varietyof experimental purposes such as bacterial infections, cancer, stemcell, and neurology research.

In some implementations, all or some of the features described may beimplemented as an integrated hardware add-on or retrofit for commercialBLI systems. In a bioluminescence bacterial study, the system may beconfigured to calculate a spatial bacterial burden or density (CFU/mm3)distribution inside a living animal. The described features are adeparture from current methods at least because: (i) they can quantitatein vivo bacterial organ burden in real-time; and (ii) reliablyco-register it to an anatomical reference.

FIG. 1A depicts a schematic view of an animal mold 100 in accordancewith an illustrative embodiment of the present disclosure. The animalmold 100 can include a top section 102 and a bottom section 104 The topsection 102 can include an animal body conforming section 110, a hingeconnection 106, and a pair of openings 107A and 107B. The bottom section104 can include an animal body conforming section 118 and acorresponding hinge connection 112. The animal mold 100 is shown in anopen position. The animal mold 100 can be generally shaped to conform tothe body of an animal, such as a rodent, such that the animal mold 100is generally body conforming to the animal.

The hinge connection 106 of the top section 102 can be engaged to thehinge connection 112 of the bottom section 104 such that the top section102 can be rotated with respect to the bottom section 104. FIG. 1Adepicts the animal mold 100 in an open position in which the bodyconforming section 110 is positioned apart from the body conformingsection 118. The top section 102 can be configured to rotate away fromthe bottom section 104 so as to provide sufficient space to place ananimal within the body conforming section 118 when in the open position.Openings 107A and 107B can be configured to receive gas from a gassupply (not shown). Gas received through the openings 107A and 107B canflow into the animal body conforming sections 110 and 118.

FIG. 1B depicts a perspective view of the animal mold 100 in an openposition in accordance with an illustrative embodiment of the presentdisclosure. In this embodiment, the animal mold 100 can be generallyshaped to conform to the body of a rodent, such as a mouse, such thatthe animal mold is generally body conforming to the animal. The animalmold 100 includes a proximal end 101 and a distal end 103. The animalmold 100 can further includes the top section 102 and the bottom section104. The top section 102 can include openings 107A and 107B, the hingeconnection 106, locking arms 108, animal body conforming section 110,and an opening 126. The bottom section 104 can include the correspondinghinge connection 112, a front plate 113, a rear plate 117, a recessedsection 114, a locking tab 116, and animal body conforming section 118.

As noted above, the animal mold 100 can be made of a solid material thatis optically transparent or at least partially transparent to facilitateoptical imaging of an animal within the interior of the animal mold 100.In non-optical imaging embodiments, the animal mold 100 can be mode of amaterial that is transparent and non-distorting to the imaging modality.The animal mold 100 can be configured so that the animal mold 100slightly compresses an animal in the interior of the animal mold 100 sothat the animal is at least partially restrained and so that the outersurface of the animal body can contact the interior surface of theanimal mold 100. As will be explained below, in some arrangements, theanimal mold 100 can provide a constant spatial frame of reference acrossdifferent animals, and can also provide a consistent surface for cameradetection points. The top and bottom sections 102, 104 of the animalmold 100 define a pre-defined interior surface 127, which can becorrelated to a given animal (for example mouse) age, strain, sex and/orweight. As shown in FIG. 1, a set of molds can be provided that cancorrespond to different animal (for example mouse) age, strain, sexand/or weight categories. In this manner, the mold 100 can hold theanimals in a fixed posture and provide a constant spatial frame ofreference across different animals within a category, and can alsoprovide a consistent surface for camera detection point.

The hinge connection 112 can be secured to the front plate 113 of thebottom section 104 at a front end of the animal mold 100. The frontplate 113 can be configured to engage a support structure of an imagingapparatus, such as a gantry, in order to maintain the animal mold 100 ina consistent position for optical imaging. The front plate 113 caninclude openings 115A and 115B (not shown in FIG. 1). Openings 115A and115B can be configured to receive gas supply components for providing agas to the animal mold 100. The hinge connection 106 of the top section102 can be engaged to the hinge connection 112 of the bottom section 104so as to form a hinge 120 having a pivot axis along line 123-123. Thetop section 102 can be rotated with respect to the bottom section 104along the pivot axis. FIG. 1B depicts the animal mold 100 in an openposition in which the body conforming section 110 is positioned apartfrom the body conforming section 118. The top section 102 can beconfigured to rotate away from the bottom section 104 so as to providesufficient space to place an animal within the body conforming section118 when in the open position.

Although hinge connections 106 and 112 are described with respect to theembodiments shown in FIGS. 1A and 1B, it is contemplated that the topsection 102 and bottom section 104 can be connected using any couplingtechnique known in the art. The top section 102 and bottom section 104may include complementary interlocking components to facilitate couplingof the top section 102 to the bottom section 104. For example, the topsection 102 and bottom section 104 may be coupled using a snap-fit orinterference fit. The top section 102 and the bottom section 104 may becoupled by one or more fasteners.

The openings 107A and 107B can be positioned distally with respect tothe hinge connection 106 and can be configured to receive a gas from agas supply (not shown). A cranial portion 109A of the body conformingsection 110 can be positioned distally from the openings 107A and 107Band at the proximal end of the body conforming section 110. Locatedbetween the openings 107A and 107B and the cranial portion 109A can be apassage 121A (not shown in FIG. 1) configured to allow gas to flow fromthe openings 107A and 107B to the cranial portion 109A when the animalmold 100 is in a closed position. The cranial portion 109A can beconfigured to receive a cranial segment of the animal. A caudal section111A is positioned at the distal end of the body conforming section 110can be configured to receive a caudal segment of the animal. The bodyconforming section 110 can be shaped and sized so as to generallyconform to a dorsal segment of the animal. The body conforming section110 further can include a bottom edge 122 configured to engage a topedge 124 of the body conforming section 118. The locking arms 108 canextend distally from the body conforming section 110.

The recessed section 114 of the bottom section 104 can be positioneddistally with respect to the hinge connection 112 and proximallyadjacent to a cranial portion 109B of the body conforming section 118.The recessed portion 114 can also be positioned below the openings 107Aand 107B, such that the recessed portion 114 can align with the openings107A and 107B when the animal mold 100 is in a closed position, as shownin FIG. 2, so that gas received through the openings 107A and 107B canenter the recessed portion 114. A passage 121B can connect the recessedportion 114 to the cranial portion 109B to allow gas to flow from therecessed portion 114 to the cranial portion 109B when the animal mold100 is in a closed position. The cranial portion 109B can be positionedat the proximal end of the body conforming section 118 and can beconfigured to receive a cranial segment of the animal. A caudal portion111B of the body conforming section 118 can be positioned at the distalend of the body conforming section 118 and can be configured to receivea caudal segment of the animal. The body conforming section 118 can beshaped and sized so as to generally conform to a ventral segment of ananimal. The locking tab 116 can be positioned distally from the bodyconforming section 118. The rear plate 117 can be located distally fromthe locking tab 116 at the distal end 103 of the bottom section 104.

FIG. 2 depicts a perspective view of the animal mold 100 of FIG. 1B in aclosed position in accordance with an illustrative embodiment of thepresent disclosure. In the closed position, the locking arms 108A and108B can detachably engage the locking tab 116. The locking arms 108Aand 108B can engage the locking tab 116 through an interference fit.When in the closed position, the bottom edge 122 of the top section 102can engage the top edge 124 of the bottom section 104 so as to form asubstantially closed inner cavity for housing an animal.

FIG. 3 depicts a side view of the animal mold 100 of FIG. 1B in theclosed position in accordance with an illustrative embodiment of thepresent disclosure. FIG. 4 depicts a top view of the animal mold 100 ofFIG. 1B in the closed position in accordance with an illustrativeembodiment of the present disclosure. FIG. 5 depicts a bottom view ofanimal mold 100 of FIG. 1B in the closed position in accordance with anillustrative embodiment of the present disclosure. FIG. 6 depicts a rearview of animal mold 100 in the closed position in accordance with anillustrative embodiment of the present disclosure. FIG. 7 depicts afront view of animal mold 100 of FIG. 1B in the closed position inaccordance with an illustrative embodiment of the present disclosure.

FIG. 8 depicts a cross sectional view of the animal mold 100 of FIG. 1Bin the closed position in accordance with an illustrative embodiment ofthe present disclosure. When in the closed position, the top section 102and bottom section 104 can define an inner cavity 128 for encapsulatingan animal. FIG. 8 further shows a gas cavity 130 defined by the recessedsection 114 and the portion of the upper section 102 including theopenings 107A and 107B. FIG. 8 further shows a passage 132 defined bypassage 121A of the upper section 102 and passage 121B of the lowersection 104. The gas cavity 130 can be configured to receive gas such asan anesthetic from the openings 107A and 107B. The gas can then flowthrough the passage 132 into the inner cavity 128. Gas can leave theinner cavity 128 through the opening 126.

FIG. 9 depicts a bottom view of the top section 102 of the animal mold100 of FIG. 1B in accordance with an illustrative embodiment of thepresent disclosure.

FIG. 10 depicts a top view of the bottom section 104 of the animal mold100 of FIG. 1B in accordance with an illustrative embodiment of thepresent disclosure.

FIG. 11A depicts a schematic view of an illustrative embodiment of agantry 150 in accordance with an illustrative embodiment of the presentdisclosure. As will be explained below, in the illustrative embodiment,the gantry 150 can include one or more mirrors that can aid in imagingan animal positioned within an animal mold that is supported by thegantry. The gantry 150 can allow for multi-orientation images. Forexample, the gantry 150 can allow for simultaneous imaging of dorsal,ventral, and side views of an animal in the animal mold 100. The gantry150 can be configured to receive an animal mold, such as the animal mold100 according to one of the embodiments described herein. The gantry 150can further be configured to receive a gas from a gas supply 281. Thegantry 150 can be connected to the gas supply 281 via supply pipes 279.In some embodiments, the animal mold 100 can be configured to receivegas flowing from the gas supply 281 to the gantry 150.

FIG. 11B depicts a perspective view of an illustrative embodiment ofgantry 150 in a closed position in accordance with an illustrativeembodiment of the present disclosure. As will be explained below, in theillustrative embodiment, the gantry 150 can include one or more mirrorsthat can aid in imaging an animal positioned within an animal mold thatis supported by the gantry 150. In some embodiments, the gantry 150 canbe configured to receive an animal mold, such as the animal mold 100according to one of the embodiments described herein. The gantry caninclude a lid 151 having a window 152, front plate 154A, a back plate154B, side walls 156A and 156B (not shown in FIG. 11B), and slidinghandle 158, and a support plate 172.

FIG. 12 depicts a perspective view of the gantry 150 in an open positionin accordance with an illustrative embodiment of the present disclosureshowing the animal mold 100 secured to the gantry 150. FIG. 12 furthershows the sliding handle 158 extended distally from the front plate154A. The sliding handle 158 can be configured to move proximallytowards or distally from the front plate 154A in response to theapplication of manual force. The sliding handle 158 can be engaged torods 160 and 162. Rod 160 is engaged to a mold support 164. The moldsupport 164 can be configured to engage the proximal end 101 of thebottom section 104 of the animal mold 100. The distal end 103 of theanimal mold 100 can be further secured to an interior surface of thehandle 158 through mold supports 182A and 182B (not shown in FIG. 12).The mold support 164 can be further configured to engage a slidingmember 166. The sliding member 166 can be slidably mounted to a track168 such that the handle 158 can translate from the open position shownin FIG. 12 to the closed position shown in FIG. 11B. Rod 162 can beconfigured to slide through an opening 170 within the front plate 154A.The support plate 172 can be secured to an exterior surface of the frontplate 154A and can be positioned such that the rod 162 can contact aportion of the support plate 172 and slide across the support plate 172.The support plate 172 can provide structural support to the rod 162.

FIG. 13 depicts a top view of the gantry 150 in a closed position inaccordance with an illustrative embodiment of the present disclosure.FIG. 14 depicts a bottom view of the gantry 150 in a closed position inaccordance with an illustrative embodiment of the present disclosure.The rod 162 is shown extending through a support section 174 of the sidewall 156A and a sliding rail 176 for supporting the movement of theanimal mold 100. When in the closed position, the rod 162 can beconfigured to be received in a recess (not shown) on an interior surfaceof the back plate 154B. FIG. 14 further shows a gas router 178 that canbe to receive gas from a gas supply, such as gas supply 281 shown inFIG. 11A, and to provide gas to the animal mold 100.

FIG. 15 depicts a top view of the gantry 150 having the lid 151 andwindow 152 removed in accordance with an illustrative embodiment of thepresent disclosure. FIG. 15 shows mirrors 180A and 180B. The mirrors180A and 180B can provide for simultaneous imaging of two differentviews of an animal within the animal mold 100. As depicted in FIG. 15,the front plate 113 at the proximal end 101 of the bottom section 104can be received by the mold support 164. The mold support 164 furtherincludes an inner cavity configured to receive a gas from gas router178. FIG. 15 further shows gas channels 184A and 184B that can beconfigured to receive gas from the inner cavity of the mold support 164.In some embodiments, tubing can be connected to the gas channels 184Aand 184B at one end and to the openings 107A and 107B of the animal mold100 at the other end in order to supply gas to the openings. FIG. 15further shows mold supports 182A and 182B extending from an interiorsurface of the handle 158. The mold supports 182A and 182B can beconfigured to receive the rear plate 117 of bottom section 104.Engagement of the animal mold 100 to the mold support 164 and moldsupports 182A and 182B can fix the animal mold 100 in place within thegantry 150 when the gantry 150 is in the closed position to allow foroptical imaging of an animal within the animal mold 100.

FIG. 16 shows a side view of the gantry 150 having the side wall 156A,the mirror 180B, lid 151 and window 152 removed in accordance with anillustrative embodiment of the present disclosure. FIG. 17 shows aperspective view of the gantry 150 having lid 151 and window 152 removedin accordance with an illustrative embodiment of the present disclosure.FIG. 18 depicts a perspective view of the gantry 150 having lid 151 andwindow 152 removed in accordance with an illustrative embodiment of thepresent disclosure.

FIG. 19 depicts a perspective view of gantry 150 having back plate 154Bremoved in accordance with an illustrative embodiment of the presentdisclosure. As depicted in FIG. 19B, the gas router 178 is positioned toengage the mold support 164 in order to supply gas thereto. The gasrouter 178 can receive gas from gas supply 281 as shown in FIG. 11A.

FIG. 20 depicts a perspective view of a two dimensional imaging gantry200 in accordance with an illustrative embodiment of the presentdisclosure. The two dimensional imaging gantry can be configured toallow for imaging of multiple animals simultaneously and forsimultaneous movement of each of the animals to provide for alternateviews. The two dimensional imaging gantry 200 can have a dockinginterface 210 and a holder 220. The holder 220 can be configured toinclude a plurality of docking stations 230A, 230B, 230C, and 230D forthe receiving animal molds. Each animal mold docking station 230A, 230B,230C, and 230D can be configured to receive an animal mold, such asanimal mold 100, depicted in FIGS. 1-10.

FIG. 21 depicts a perspective view of the docking interface 210 of twodimensional imaging gantry 200 in accordance with an illustrativeembodiment of the present disclosure. The docking interface 210 includesa recess 215 configured to receive the holder 220. The docking interface210 can be configured to secure the holder 220 via an interference fitor one or more fasteners. The docking interface 210 can be configured toconnect with a system specific interface of an imaging system, forexample, by pins, crevices, or any other technique known in the art.

FIG. 22 depicts a top view of the holder 220 in accordance with anillustrative embodiment of the present disclosure showing severalinternal components. FIG. 22 shows an animal mold 240 within chamber230A of the holder. The animal mold includes a front connection 242 anda rear connection 244 for connecting to an interior front wall 246 andan interior rear wall 248, respectively, of the chamber 230A.

The gantry 200 can be secured to an imaging apparatus, in which thegantry 200 can be rotated by at least 180° in order to image the animalswithin the holder from at least two different views. The gantry 200 canbe connected to a gas anesthesia supply via flexible tubing, whichallows movements of the gantry while connected to the gas supply. Insome embodiments, the gantry 200 allows for imaging of a dorsal view anda ventral view. In some embodiments, the gantry 200 allows for imagingof opposing lateral views. In some embodiments, the gantry 200 allowsfor imaging of a cranial view and a caudal view.

FIG. 23 depicts a perspective view of the holder 220 connected to a gassupply bar 250 in accordance with an illustrative embodiment of thepresent disclosure. The gas supply bar can be configured to engage withthe docking interface 210 Extending from the gas supply bar 250 are gassupply pipes 252A and 252B, which extend to an inner cavity of theholder 220. Tubes 254A and 254B extend from the inner cavity of theholder 220 to openings within the animal mold 240, which can be similarto openings 107A and 107B of animal mold 100, as discussed above withrespect to FIGS. 1-10. Gas, such as anesthesia can flow from the gassupply bar 250 through the pipes 252A and 252B into the inner cavity ofthe holder 220. From the inner cavity of the holder 220, the gas canflow through the tubes 254A and 254B to the animal mold 240. Opticalblinds (screens) may further be positioned between chambers 230A-D inorder to prevent light leakage from adjacent animal molds, which are anambient light source. An optical blind 256 is shown positioned betweenchambers 230B and 230C.

FIGS. 24-30 depict an embodiment of an animal mold 300 in accordancewith an illustrative embodiment. FIGS. 24, 25, 26, 27, 28, 29, and 30depict a first perspective view, a second perspective view, across-sectional view, a front view, a rear view, a bottom view of a topsection, and a top view of a bottom section, respectively, of the animalmold 300 in a closed position in accordance with an illustrativeembodiment. In this embodiment, the animal mold 300 can be generallyshaped to conform to the body of a rodent, such as a mouse, such thatthe animal mold 300 is generally body conforming to the animal. Theanimal mold 300 can include a proximal end 301 and a distal end 303. Theanimal mold 300 can further include a top section 302 and a bottomsection 304. The top section 302 can include a body conforming section310 and the bottom section 304 can include a body conforming section318. The proximal end 301 of the animal mold 300 can include hingeconnections 306A, 306B, 312A and 312B, a proximal passage 330, a gasdelivery passage 305, a pair of scavenging passages 307A and 307B, aperipheral edge 313, a computer readable label 328, and a pair of wings315A and 315B.

The hinge connections 306A and 306B, positioned on the top section 302can be engaged to the hinge connections 312A and 312B, respectively,positioned on the bottom section 304 so as to form hinges 320A and 320Bhaving a pivot axis along line 323-323. The top section 302 can berotated with respect to the bottom section 304 along the pivot axisbetween the closed position shown in FIG. 24 and an open position. Thetop section 302 can be configured to rotate away from the bottom section304 so as to provide sufficient space to place an animal within the bodyconforming section 318 when in the open position.

The passages 330, 305, 307A, and 307B can be formed from sections of theupper portion 302 and lower portion 304 of the animal mold 300 engagingone another when the animal mold 300 is in the closed position. Passages305, 307A, and 307B can be positioned within the passage 330 distallyfrom a proximal end of the passage 330. Passage 330 can be configured toreceive one or more gas supply components. Passage 305 can be configuredto receive gas from a gas supply component at its proximal end 331A andto deliver gas to the body conforming sections 310 and 318 at its distalend 331B. The distal end 331B of the passage 305 can open to theproximal end of the body conforming sections 310 and 318. Gas can flowthrough the body conforming sections 310 and 318 and out of an opening326 positioned at the distal end of the body conforming sections 310 and318. Passages 307A and 307B can be configured for scavenging gas flowingout of the animal mold 300. Scavenging may be active, using a suctionmechanism, or passive, allowing for the flow of gas without the applyingsuction.

The peripheral edge 313 and wings 315A and 315B of the animal mold 300can be configured to engage a support structure of an imaging apparatus,such as a gantry, in order to maintain the animal mold 300 in aconsistent position for optical imaging.

The computer readable label 328 can be a barcode, a QR code, or anyother computer readable label known in the art. The computer readablelabel 328 can be configured such that the computer readable label isdetectable in a photographic image captured by an optical imagingsystem, such as, for example, imaging gantry 150, imaging gantry 200,imaging gantry 350, and imaging gantry 400 as described herein. Thecomputer readable label 328 can be configured to be processed by animaging recognition software following capture of an image by theoptical imaging system, for example, to extract data from the computerreadable label 328. In some embodiments, the computer readable label 328can include fields of black and white areas, such as quadrants or bars.Fields of black and white bars may be preferable for creating imagecontrast for detection by the imaging recognition software. In someembodiments, the computer readable label 328 can encode at least 64 bitinformation.

The computer readable label 328 can store or be associated with animalmold data, imaging data, customer data, animal specimen data, or anyother data relevant to imaging an anatomical structure. For example, acomputer readable label 328 can store or be associated with data relatedto the size of the animal mold 300 and the shape of the animal mold 300.As described herein, animal molds may be produced at various sizes andshapes to accommodate different strains and sizes of animal specimens.Specimen data can include a size, sex, and/or species information for ananimal specimen. A computer readable label 328 can also store or beassociated with customer/client identification data. This data canfacilitate tracking of personalized settings and use in an imagingsystem. In some embodiments, multiple computer readable labels can beattached to or formed in the animal mold 300 in multiple positions. Forexample, a first computer readable label can be positioned on the topsection 302 and a second computer readable label can be placed on thebottom section 304 to allow for identification of the differentcomponents of the animal mold 300 and/or to allow for the identificationof images captured by an imaging device as dorsal or ventral views. Insome embodiments, the computer readable label 328 can be attached to orformed in the animal mold 300 at a position in close proximity to thebody conforming sections 310 and 318. Placement of the computer readablelabel 328 close to the body conforming sections 310 and 318 can allowfor a smaller camera field-of-view (“FOV”) for capturing the computerreadable label 328 and the animal specimen within the animal mold 300.For example, the computer readable label 328 may be positioned close toa cranial section of the body conforming sections 310 and 318. In someembodiments, the computer readable label 328 can be positioned close inproximity to a section of the animal mold 300 intended to receive a noseor snout of an animal specimen placed therein.

At the distal end 303 of the animal mold 300, the bottom section 304 caninclude a plate 317. The plate 317 can include a pair of locking members314A and 314B extending vertically, a recess 316, and a crossbar 321. Atthe distal end 303 of the animal mold 300, the top section 302 caninclude a plate 325 having tabs 308A and 308B and a curved crossbar 319.

The plate 317 can be configured to engage a support structure of animaging apparatus, such as a gantry, in order to maintain the animalmold 300 in a consistent position for optical imaging. The recess 316can be configured to receive a fastener in order to further secure theanimal mold 300 to an imaging apparatus. A bottom surface of the plate325 can be shaped to detachably engage a top surface of the plate 317when the animal mold 300 is in the closed position. Locking member 314Aand tab 308A can comprise complementary connection components fordetachably securing to one another. Likewise, locking member 314B andtab 308B can comprise complementary connection components for detachablysecuring to one another. In some embodiments, each locking member 314Aand 314B can include a recess on an interior surface of the lockingmember configured to receive a protrusion on the exterior surface of thetabs 308A and 308B, respectively. In some embodiments, a force can beapplied to each tab 308A and 308B in a direction away from thecorresponding locking members 314A and 314B to disengage the tabs 308Aand 308B from the locking members 314A and 314B.

An upper surface of the crossbar 321 and lower surface of the crossbar319 can define an opening 329 when the animal mold 300 is in the closedposition. The opening 329 can be configured to secure the tail of ananimal, such as a rodent. Securing the tail of an animal can facilitatetail vein catheterization, which may provide an additional mechanism forapplying anesthesia to the animal.

As noted above, the animal mold 300 can be made of a solid material thatis optically transparent or at least partially transparent to facilitateoptical imaging of an animal within the interior of the animal mold 100.In non-optical imaging embodiments, the animal mold 300 can be mode of amaterial that is transparent to the imaging modality. The animal mold300 can be configured so that the animal mold 300 slightly compresses ananimal in the interior of the animal mold 300 so that the animal is atleast partially restrained and so that the outer surface of the animalbody can contact the interior surface of the animal mold 300. In somearrangements, the animal mold 300 can provide a constant spatial frameof reference across different animals, and can also provide a consistentsurface for camera detection points. The top and bottom sections 302 and304 of the animal mold 300 define a pre-defined interior surface 327,which can be correlated to a given animal (for example mouse) age,strain, gender and/or weight. As shown in FIGS. 24-28, a set of moldscan be provided that can correspond to different animal (for examplemouse) age, strain, gender and/or weight categories. In this manner, themold 300 can hold the animals in a fixed posture and provide a constantspatial frame of reference across different animals within a category,and can also provide a consistent surface for camera detection point.

A cranial portion 309A of the body conforming section 310 can bepositioned distally from the distal end 331B of the passage 305 and atthe proximal end of the body conforming section 310 and can beconfigured to receive a cranial segment of the animal. A cranial portion309B can be positioned at the proximal end of the body conformingsection 318 and can be configured to receive a cranial segment of theanimal. The distal end 331B of the passage 305 can be configured toallow gas to flow to the cranial portions 309A and 309B when the animalmold 300 is in a closed position. A caudal section 311A can bepositioned at the distal end of the body conforming section 310 can beconfigured to receive a caudal segment of the animal. The bodyconforming section 310 can be shaped and sized so as to generallyconform to a dorsal segment of the animal. A caudal portion 311B of thebody conforming section 318 can be positioned at the distal end of thebody conforming section 318 and can be configured to receive a caudalsegment of the animal. The body conforming section 318 can be shaped andsized so as to generally conform to a ventral segment of an animal. Thebody conforming section 310 can further include a bottom edge 322configured to engage a top edge 324 of the body conforming section 318.

FIGS. 31-38 depict an embodiment of a gantry 350 in accordance with anillustrative embodiment of the present disclosure. The gantry 350 caninclude many components that are the same as or similar to components ofthe gantry 150 depicted in FIGS. 11A-19. FIG. 131 depicts a perspectiveview of the gantry 350 in accordance with an illustrative embodiment ofthe present disclosure. As will be explained below, in the illustrativeembodiment, the gantry 350 can include one or more mirrors that can aidin imaging an animal positioned within an animal mold that is supportedby the gantry. The gantry 350 can be configured to receive an animalmold, such as the animal mold 300 according to one of the embodimentsdescribed herein. The gantry 350 can further be configured to receive agas from a gas supply.

The gantry 350 can include a lid 351 having a window 352, a front plate354A, a back plate 354B, side walls 356A and 356B, and a sliding handle358. In some embodiments, the sliding handle 358 can be configured tomove proximally towards or distally from the front plate 354A inresponse to the application of manual force. The handle 358 may beoperated in a similar manner to the handle 158 described above withrespect to FIGS. 11A-19.

FIG. 32 depicts a top sectional view of the gantry 350 in accordancewith an illustrative embodiment of the present disclosure showing theanimal mold 300 secured to the gantry 350. The sliding handle 358 can beengaged to a rod 360. Rod 360 can be engaged to a mold support 364,which can be configured to support the proximal end 301 of the animalmold 300. The distal end 303 of the animal mold 300 can be furthersecured to an interior surface of the handle 358 through a mold support382. The distal end 303 can be received by a recess 383 of the moldsupport 382. Engagement of the animal mold 300 to the mold support 364and mold support 382 can fix the animal mold 300 in place within thegantry 350 when the gantry 350 is in the closed position to allow foroptical imaging of an animal within the animal mold 300. The moldsupport 364 can be further configured to engage a sliding member 366that can be slidably mounted to a track 368 such that the handle 358 cantranslate from an open position to a closed position.

FIG. 32 further shows a gas output nozzle 379 configured to receive gasfrom the scavenging passages 307A and 307B. The gas output nozzle 379can be engaged to the back plate 354B and can extend into an opening inthe back plate 354B to receive gas from the scavenging passages 307A and307B. The gantry 350 can further include a gas block 384. The gas block384 can include a mount 385 and a mold receiving channel 387 configuredto engage the proximal end 301 of the animal mold 300. The mount 385 canbe secured to the interior wall of the front plate 354A by one or morefasteners, or can be integrally formed with the front plate 354A. Thechannel 387 can be shaped and sized to receive the hinges 320A and 320Band a proximal end of the passage 330. The channel 387 can be configuredsuch that when the proximal end 301 of the animal mold 300 is receivedin the channel 387, the peripheral edge 313 fits within channel 387 toseal or substantially seal the channel 387. Engagement of the peripheraledge 313 within the channel 387 can allow gas to flow between the block384 and the animal mold 300 while preventing gas from leaking out of thechannel 387 and into the gantry 350. The channel 387 can further includea pair of slots 373A and 373B (not shown in FIG. 32) configured toreceive wings 315A and 315B, respectively, of the animal mold 300. Theslots 373A and 373B can provide an indexing feature to assure alignmentof the animal mold 300 with the gas block 384.

FIG. 32 further depicts mirrors 380A and 380B. The mirrors 380A and 380Bcan provide for simultaneous imaging of two different views of an animalwithin the animal mold 300.

FIG. 33A shows a side view of the gantry 350 having the side wall 356A,the mirror 380B, lid 351 and window 352 removed in accordance with anillustrative embodiment of the present disclosure. FIG. 33 further showsthe wing 315A of the animal mold 300 engaged with the slot 373A and afastener 341A. The fastener 341A can be positioned distally from thewing 315A when the wing 315A is engaged with the slot 373A and cansecure the animal mold 300 in position with the gas block 384. Thefastener 341 can prevent distal movement of the wing 315A in order toprevent distal movement of the animal mold 300. A fastener 341B (notshown in FIG. 33A) can be positioned behind the wing 315B when the wing315B is positioned within the slot 373B and can function to preventdistal movement of the wing 315B in order to prevent distal movement ofthe animal mold 300. The gantry 350 further includes a gas supply nozzle378 engaged to the back plate 354B. The gas supply nozzle 378 can beconfigured to provide gas into an opening in the back plate 354B inorder to direct the gas to the passage 305.

FIG. 33B shows a schematic side view of the gantry 350 having severalcomponents removed. A gas supply source 342 can be connected to the gassupply nozzle 378 by a pipe 346. Gas can flow from the gas supply source342 to the gas supply nozzle 378. The gas can then flow into the nozzle378 and through a conduit 348 in the back plate 354B. The gas can flowthrough the back plate 354B into a nozzle 377 extending from theinterior wall of the back plate 354B and through the gas block 384. Thenozzle 377 can direct gas to the gas delivery passage 305 of the animalmold 300. A vacuum source 344 can be connected to the gas output nozzle379 by a pipe 347. The gas output nozzle 379 is connected to a conduit349 within the back plate 354B. The conduit 349 is connected toscavenging channels 363A and 363B positioned within the gas block 384.Scavenging channels 363A and 363B are connected to scavenging passages307A and 307B, respectively, of the animal mold 300. A vacuum force canbe applied by the vacuum source 344 to draw excess gas into the passages307A and 307B from the gantry 350, including excess gas escaping theanimal mold 300.

FIG. 34 shows a perspective view of a section of the gantry 350 havingthe lid 351 and window 352 removed. FIG. 34 shows the wing 315B engagedwith the slot 373B. FIG. 34 further shows the fastener 373B positionedbehind the wing 315B.

FIG. 35 shows a perspective view of a section of the gantry 350 havingthe lid 351, window 352, and animal mold 300 removed. FIG. 35 shows thenozzle 377 extending from the interior wall of the back plate 354B andthrough the gas block 384. The nozzle 377 can be configured to receivegas from the gas supply nozzle 378 and to direct gas to the gas deliverypassage 305. The gas block 384 can also include scavenging channels 363Aand 363B configured to receive gas from the scavenging passages 307A and307B. As described above, gas can flow from the scavenging passages 307Aand 307B through the scavenging channels 363A and 363B and to the gasoutput nozzle 379.

FIG. 36 shows a perspective view of a section of the gantry 350 havingthe lid 351 and window 352 removed. FIG. 36 shows the distal end 303 ofthe animal mold 300 positioned in the recess 383 of the mold support382. The distal end 303 of the animal mold 300 can be secured to themold support 382 by a fastener 345 extending through the recess 316 ofthe animal mold 300. FIG. 37 shows a perspective view of a section ofthe gantry 350 having the lid 351, window 352, and animal mold 300removed. The recess 383 of the mold support 382 includes an aperture 343for receiving the fastener 345.

FIG. 38 shows a perspective view of a section of the gantry 350 havingthe lid 351 and window 352 removed and showing an alternative embodimentof a gas block 390. The gas block 390 can include many of the same orsimilar components to the gas block 384. The gas block 390 furtherincludes a wire latch 340 extends from the channel 387. The wire latch340 can extend around a distal portion of the wings 315A and 315B whenthe wings 315A and 315B are engaged with the slots 373A and 373B. Thewire latch 340 can secure the animal mold 300 in place when the animalmold 300 is engaged with the gas block 390. The wire latch 340 canprevent distal movement of the wings 315A and 315B in order to preventdistal movement of the animal mold 300 when the wire latch 340 engagesthe animal mold 300. In some embodiments, the wire latch 340 can supportthe animal mold 300 without the use of the mold support 364.

FIG. 39 depicts a perspective view of an alternative embodiment of a twodimensional imaging gantry 400 in accordance with an illustrativeembodiment of the present disclosure. The two dimensional imaging gantry400 includes a lid 425 including a window 416 and recesses 417A and417B. The gantry 400 further includes a docking interface 410 having abase plate 415, a first side wall 411 and a second side wall (not shown)positioned opposite the side wall 411. Latches 413A and 413B are securedto the second side wall and the first side wall 411, respectively. Thelatches 413A and 413B are configured to engage with the recesses 417Aand 417B respectively, of lid 425 to secure the lid 425 to first sidewall 411 and the second side wall. The gantry 400 further includes aholder 420 positioned on top of the bottom plate 415 and between thefirst side wall 411 and second side wall. The gantry 400 can alsoinclude a tail stop 418 configured to prevent a tail from an animalwithin the gantry from extending outside of the gantry.

FIG. 40 shows a perspective view of the gantry 400 with the lid 425,first side wall 411, second side wall, and tail stop removed. The holder420 can be configured to secure a plurality of animal molds 430A-430D inposition within the gantry 400. The animal molds 430A-430D can includethe same components or generally similar components to the animal mold300 described with respect to FIGS. 24-29. The holder 420 can furtherinclude a plurality of optical mirrors or optical blinds 456A-C, and aplurality of apertures 441A-D, each aperture configured to receive atail of an animal in the molds 430A-D. The holder can further includesgas blocks 440A-D. Gas blocks 440A-D can include the same components orgenerally similar components to gas block 390 depicted in FIG. 38. In analternative embodiment, the gas blocks 440A-D can include the samecomponents or generally similar components to gas block 384 depicted inFIGS. 30-37.

FIG. 41 shows a perspective sectional view of the gantry 400 with thelid 425, first side wall 411, second side wall, and tail stop removed.The holder 420 further includes a plurality of mold supports 442A-Dconfigured to engage and secure a distal end of the molds 430A-D. Themold supports 442A-D can include many similar components to the moldsupport 382 as depicted in FIGS. 30-37. For example, the mold supports442A-D can include a recess and an aperture for receiving a fastener.

FIG. 41 further depicts a gas manifold 450 having a supply nozzle 452, aknob 454, and an output nozzle 458. The manifold 450 can includechanneling for directing gas between the nozzles 452 and 458 and theanimal molds 430A-D. The supply nozzle 452 can be configured to receivegas from an external source for supply to the gas blocks 440A-D and intothe molds 430A-D. The output nozzle 458 can be configured to receive gasscavenged through scavenging passages of the animal molds 430A-430D forremoval. The knob 454 can be configured to allow for the opening and/orclosing of one or more channels within the manifold 450 to allow orprevent the flow of gas.

The gantry 400 can be secured to an imaging apparatus, in which thegantry 400 can be rotated by at least 180° in order to image the animalswithin the holder from at least two different views. In someembodiments, the gantry 400 allows for imaging of a dorsal view and aventral view. In some embodiments, the gantry 400 allows for imaging ofopposing lateral views. In some embodiments, the gantry 400 allows forimaging of a cranial view and a caudal view.

FIG. 42 depicts a flowchart of a process 500 for collecting opticalimaging data of an animal in accordance with an illustrative embodimentof the present disclosure.

The process 500 beings at a step 510, wherein an animal is placed in ananimal mold, such as animal molds 100 as depicted in FIGS. 1-10, animalmold 240 as depicted in FIGS. 20-23, animal mold 300 as depicted inFIGS. 24-30, or animal molds 440A-D as depicted in FIGS. 39-41. Afterthe animal is placed in the animal mold, the animal mold by be movedfrom an open position to a closed position in order to at leastpartially immobilize the animal in a geometrically defined position. Insome embodiments, the animal mold may be placed in a gantry, such asgantry 150 shown in FIGS. 11-19, gantry 200 shown in FIGS. 20-23, gantry350 shown in FIGS. 31-38, or gantry 400 shown in FIGS. 39-41.

After the animal is placed in the animal mold, the process 500 moves toa step 520, wherein a gas, such as anesthesia, is supplied to the animalmold. The anesthesia can act to further immobilize the animal within themold to prevent movement or distress while image is performed. The gascan be supplied to the animal mold from a gas supply, such as gas supply281, gas supply nozzle 378, supply nozzle 452, or a gas supply bar, suchas gas supply bar 250.

After gas is supplied to the animal mold, the process 500 moves to astep 530 wherein optical imaging is performed. Optical imaging caninclude two dimensional bioluminescence imaging or three dimensionalbioluminescence tomography. In some embodiments, after optical imagingis performed, data can be collected from one or more imaging devices andcompared between two or more animals or between multiple images takenfrom the same animal.

The embodiments described herein can be used with image processingdevices or systems for the collection and analysis of data related to animaged animal. For example, the embodiments described herein can be usedin with the image processing devices or systems described herein such asthose shown and described with respect to FIGS. 43-54.

FIG. 43 is a system diagram showing several components that may beincluded in a cross-correlation imaging system. The system 600 mayinclude a positioning assembly 602, an imaging device 604, and an imageprocessing server 700. As shown in FIG. 43, the imaging device 604 andthe image processing server 700 are in data communication via a network608. The network 608 may include one or more of a LAN, WAN, cellularnetwork, satellite network, and/or the Internet. Connection to thenetwork 608 may be, for example, via a wired, wireless, or combinationof wired and wireless, communication link. The communications via thenetwork 608 may include messages. The messages may be formatted andtransmitted according to a standardized protocol such as TCP/IP, HTTP,FTP, or the like.

The positioning assembly 602 is configured to maintain a subject in aknown pose. One example of a positioning assembly is abody-shape-conforming animal mold such as those described in U.S. patentapplication Ser. No. 14/319,504 entitled “SYSTEM, METHOD ANDCOMPUTER-ACCESSIBLE MEDIUM FOR PROVIDING BODY-CONFORMING ANIMAL BED,CALIBRATION CELL AND ORGAN PROBABILITY MAP FOR QUANTITATIVE OPTICALIMAGING OF ANATOMICAL STRUCTURES”, the disclosure of which isincorporated by reference in its entirety. The positioning assembly 602may be implemented as a body conforming animal mold such as those shownand described herein with respect to FIGS. 1-42. To facilitate imaging,the positioning assembly 602 may be optically transparent. The subject,when placed into the positioning assembly 602, may be in an immobilizedand geometrically defined position without the need for any additionalsurface registration hardware to detect the pose. As such, thepositioning assembly 602 provides a defined geometry that can bedetected. For example, in BLT experiments, the subject's surfacegeometry may need to be determined for proper light propagationmodeling. The light propagation model may be based on simplifiedspherical harmonics (SP_(N)), which is a high-order transport that canbe applied to the entire bioluminescence spectrum. This may afford ahigh level of quantification accuracy in the BLT experimental data.Including that the SP_(N) solutions can increase the image processingaccuracy over other implementations, such as those relying on diffusion,when considering strongly absorbing tissue.

The positioning assembly 602 and the subject therein may be detectedusing a sensor 606 coupled with or included in the imaging device 604.Examples of the sensor 606 include positron emission tomography, singlephoton emission counting tomography, magnetic resonance tomography,x-ray computed tomography, and optical fluorescence tomography. Thesensor 606 may be configured to provide image data for cross-correlationas described herein. It will be understood that the imaging device 604may be coupled with or include multiple sensors, of the same ordifferent types.

The imaging device 604 may be configured to label image data files. Forexample, the image data files may be named using a convention thatpermits efficient identification of the experiment, subject, and/orimaging device. The imaging device 604 may be configured to bundlemultiple image data files for transmission to the image processingserver. The bundling may include collecting image data based on one ormore of subject identifier, subject weight, spectral band, experimentalnotes, calibration files, time of luciferin injection, imaging time, orsome combination of one or more of these factors.

In some implementations, the imaging device 604 may include an interfaceto receive an input from a user indicating the initiation of an imagingsession. The imaging device 604 through a hardware and/or softwareadd-on may be configured to provide image data to the image processingserver 700. In such implementations, the imaging device 604 may receivean input indicating where the acquired image data should be saved. Whena new dataset shows up in the specified location, the interface of theimaging device 604 may present an interface to receive informationidentifying a database line ID such as a mouse ID or calibration set)which with to associate the acquired image data and record thisinformation in association with the image data. Once the acquisition iscompleted, the imaging device 604 may bundle the data stored in thespecified location. The completion of the acquisition may be specified,for example, through an additional input received by the imaging device604. Upon completion of the bundling, the imaging device 604 maytransmit the bundled image data to the image processing server 700 forfurther processing as described below. The image processing server 700may store the received image data in the image data store 616.

Having the subject in a known pose may also permit generation ofinformation used for the reconstruction and/or cross-correlation ofexperimental images prior to receiving an image for processing. Forexample, a kernel matrix may be used for recurring BLT imagingreconstructions. Generating the kernel matrix may include usingexpectation-maximization (EM), an Algebraic Reconstruction Technique(ART), or other image reconstruction techniques. Generating this kernelmatrix can be resource intensive (e.g., time, power, processor,bandwidth, memory, etc.). As such, generating the kernel matrix as partof image processing can introduce resource constrains on the system 700.In contrast, the described features allow the generation of an accuratekernel matrix for subsequent image data processing.

Having the subject in a known pose also enables the construction of asubject atlas for one or more subject types. One example of a subjectatlas is a mouse atlas. FIG. 43 shows a subject atlas data store 614that may store subject atlas data used by the image processing server610. The subject atlas data store 614 may include one or more positiondefinitions. A position definition may identify, for a subject havingcertain attributes, one or more locations for features of interest. Forexample, for an animal subject, a position definition may indicate, foran animal in a known pose, where the animal subject's organ (e.g.,liver) is located. In some implementations, a position definition may berepresented as an organ probability map.

An organ probability map (OPM) may include a statistical representationof the ‘average’ spatial organ distribution of a given pool of animalswith same or similar (e.g., within a tolerance) attributes such as bodyweight and gender while taking the biological variation across differentanimals into account. A particular position definition may be associatedwith a specific positioning assembly (e.g., body-shape-conforming animalmold) for a defined attribute (e.g., body weight). The atlas dependsneither on posture registration hardware or complex deformationmodeling, is operator-independent without manual surface alignment, andprovides an instantaneous anatomical reference for the imagereconstruction, such as BLT reconstruction. Some implementations of theOPM may include one thousand data points for each view of the subject(e.g., the animal holder can be rotated to image dorsal, ventral, and/orside views of the subject). For three-dimensional implementations, theOPM may include 10,000 data points. Thus, the imaging results provide amore realistic representation of the expected organ distribution than anatlas merely based on a single mouse. Furthermore, the surface shape ofthe OPM can be co-aligned with the positioning assembly and without theneed for additional surface registration methods or manual observerinteraction. Still further, where a study has a control and multiplecohorts with an N=10-50 and images captured more than 10 times yieldsmillions of data points. Not only does this provide rich data, the datais also standardized for cross-correlation with data collected fromprevious or future studies.

Some cross-correlation implementations align a subject atlas eithermorphologically or by the aid of some additional registration hardwareand software to the silhouette of a mouse with arbitrary posture andweight. However, these implementations may be computationally demandingbecause they uses an elastic tissue deformation model that overlays adigitally deformed mouse atlas with the measured surface geometry ofeach individual animal. Besides providing an anatomical reference for agiven strain and body weight, the OPM may also provide spatiallynon-uniform optical parameter distributions of tissue that is needed forthe light propagation model. The known absorption (μ_(a)) and scattering(μ_(s)′) coefficients of various organ tissue can be assigned todifferent locations (e.g., organs or other anatomical features) definedby the OPM.

For instance, in bioluminescence tomography experiments, having thesubject in a known pose can also enables the use of non-uniform opticalproperty maps for the BLT algorithm while significantly enhancing theimage reconstruction accuracy. For example, because the experimentalimages are captured in a known pose, non-uniformities in the images canbe accounted for based on the pose and attributes of the subject.

Further corrections to the captured experimental data can be achievedbased on the pose and attributes of the subject. For example, ahigh-order transport model for light propagation can be used for thebioluminescence tomography analysis. The model may be generated usingstatistical Monte-Carlo methods or other computationally intensivephoton transport methods (finite-difference or finite-element methodsfor solving the Boltzmann transport equation). Since an output of amodel depends on the quality of the values input to the model, havingwell-defined and constrained inputs, such as those afforded by havingthe subject in a known pose and known attributes, can yield a moreaccurate model. Furthermore, the amount of computation needed for photontransport methods can be reduced. For example, the photon transportmethod for a subject imaged in a pose as described herein can be solvedonce to generate an image kernel for each animal pose. This cansignificantly reduce the computational burden of subsequent imagereconstructions using the same image kernel.

The position definitions may be generated through direct measurement ofsubjects in the associated poses. In some implementations, the positiondefinitions may be interpolated based on other pose definitions. Forexample, from a first position definition, a second position definitionmay be generated by scaling across subjects with different weights(e.g., 14 g-36 g) and/or within a similar weight range (±1 g), age,gender (male/female), strain, or other variable s of a subject.

As shown in FIG. 43, one positioning assembly 602 is provided. In someimplementations, a set of positioning assemblies may be provided wherebyeach positioning assembly is associated with different attributes of asubject (e.g., weight, age, gender, strain, etc.).

The system 600 may also include an in vitro optical calibrator. The invitro optical calibrator processes the experimental data captured toprovide an output that mimics the average spectral optical properties ofa mouse. The in vitro optical calibrator may enable the generation of acalibration factor needed for in vivo quantification of experimentalvalues such as bacterial density. In one implementation, the in vitrooptical calibrator enables the translation of a physical quantity (e.g.,photons cm³ s⁻¹) into a biologically relevant quantity (e.g., number ofcolony forming units (CFU) number of cancer cells, etc.). Some BLIsystems are configured to measure the light intensity (photons s⁻¹cm⁻²)at the tissue surface. However, this intensity at the tissue surface maynot provide an accurate and/or direct determination of a biologicallyrelevant quantity (e.g., the bacterial density (CFU/mm³)) of the tissue.The in vivo bacterial density calculation may include a calibrationfactor to transform the physical quantity (e.g., photons s⁻¹cm⁻³) intothe sought biological indicia (e.g., CFU count). Such calibration factorcan be determined with the in vitro optical calibrator. The in vitrooptical calibrator may include of synthetic tissue phantom material thatmimics some average optical tissue properties at four different spectralwindows of the bioluminescence spectrum. The calibration factor may thenbe generated by using a known amount of CFU placed inside the calibratorand reconstructing its photon emission density.

In some implementations, the system 600 may be portable and configuredto be plugged into an optical small animal imaging system.Bioluminescence images may be bundled and sent by the system to acentralized image processing server via a network. The image processingserver may be configured to perform the BLT reconstruction and automatedimage analysis software. The image processing server maybe configured togenerate a final and comprehensive study report. The report may beautomatically transmitted to a user device such as the imaging device,hardware add-on/retrofit thereof, or other computing device.

FIG. 44 shows a functional block diagram showing an example imageprocessing server. The image processing server 700 includes an imagereceiver 702, a position detector 704, an image processor 706, aprocessing unit 708, a memory 710, and an imaging controller 780. Theelements included in the image processing server 700 may be coupled by abus 790. The bus 790 may be a data bus, communication bus, or other busmechanism to enable the various components of the image processingserver 700 to exchange resources (e.g., power) and/or information.

The processing unit 708 may be configured to coordinate the activitiesof the image processing server 700. For example, an image may bereceived by the image receiver 702. The image receiver 702 may obtainthe image data for the image and receive instructions from theprocessing unit 708 indicating where the image data should be sent(e.g., memory 710, image data store 616, image processor 706, etc.). Insome implementations, the image receiver 702 may forward the image datato the processing unit 708 for further processing.

The image receiver 702 may be configured to receive the image data viawired, wireless, or hybrid wired-wireless channels. The image datareceived by image receiver 702 may include two-dimensional image data orthree-dimensional image data. In some implementations, the imagereceiver 702 may receive information about the imaging device and/orsensor that generated the image data. The image receiver 702 may beconfigured to receive image data represented in a variety ofmachine-readable formats such as JPG, TIFF, GIF, SVG, PNG, XML, DICOM,or the like. The image data may include a representation of a singleimage or of a series of images (e.g., video, time lapse sequence, etc.).

An image data set may include or be accompanied by meta data. The metadata may include information which further describes the experimentalsetting, the animal (e.g., age, weight, sex), the administered therapy,the instrument settings (e.g., focal length, shutter time, F-ratio),project name, study time point, or the like.

The position detector 704 may be configured to identify a position of asubject shown in an image included in the image data. In someimplementations, the position detector 704 may identify the positionthrough analysis of an image included in the image data. For example,the image may include a machine readable identifier (e.g., barcode, QRcode, icon, graphic, text). Embodiments of a machine readable identifierare described herein with respect to FIGS. 24-41. The identifier may beassociated with a particular positioning assembly. By identifying thepositioning assembly used when capturing the image, the position may beidentified. The identification process may include querying the subjectatlas data store 614 or other data storage device that includes a datarecord associating the machine readable identifier with a positioningassembly. In some implementations, an image processing configuration maybe provided to the image processing server 700. The image processingconfiguration may include the associations between the identifier andthe positioning assembly.

The position detector 704 may receive a message from the processing unit708 to initiate position detection for an image identified in themessage. The identification of the image may be through a unique imageidentifier that can be used to identify the image data including theimage within the image data store 616. In some implementations, it maybe desirable to include the position information with the image data(e.g., as metadata). In such implementations, the position informationfor the image data may be generated using image data stored in a bufferor the memory 710 before storage in the image data store 616. In suchimplementations, the processing unit 708 may provide a memory locationfrom which the position detector 704 can access the image data to beanalyzed.

The position detector 704 may be calibrated prior to an imaging session.The calibration may utilize a calibration device (not shown). Thecalibration device may include unique image position identifiers thatcan be correlated with position information detected by the positiondetector 704. The spatial location of the calibration image identifiersmay be stored on a storage device and made available for use forsubsequent imaging sessions.

The image processor 706 is configured to process image data to generatean imaging result. The image processor 706 may receive a message fromthe processing unit 708 that image data is available for processing. Themessage may include information the image processor 706 is configured touse to obtain the image data. The information may include an identifierfor the image data or a memory location where the image data is located.In some implementations, the image processor 706 may be configured tomonitor a memory location for arriving image data. For example, adirectory may be specified within the memory 710 where image data (oridentifiers therefor) that is ready for processing is placed. As theimage processor 706 generates imaging results for the respective images,the image data may be removed from the directory. The directory may bespecified via the image processing configuration. The imaging resultsmay be stored in the image data store 616 in association with the imagedata.

The image processor 706 may be configured to obtain a processingprotocol to process the image data. Because the image processing server700 may be used to process image data from a variety of sources, for avariety of experiments, processing protocols may be provided to allowdynamic adjustment of the image processing server 700 to analyzedifferent image data. A processing protocol may be specific to one ormore of: an experiment, an image data type, a positioning assembly, alocation, or other feature detectable by or provided to the imageprocessing server 700.

For an identified experiment and image data type, the processingprotocol may identify a portion of the image data the image processor706 may use for generating the image processing result. For example, inan experiment testing delivery of a drug to the liver, it may bedesirable to focus the image data processing on the portion of the imagedata corresponding to the liver.

The processing protocol may indicate specific values for processing theimage data. For example, it may be desirable to provide imaging resultsthat indicate levels of bacteria within an area. The levels may beidentified as ranges of values. These ranges of values may be specifiedusing the processing protocol. Accordingly, when the image processor 706analyzes image data at a location, the output of the analysis uses theimage data as categorized into one of the ranges.

It will be appreciated that in addition to specific values, thecombination of values may also be specified using a processing protocol.For example, it may be desirable to provide a ratio of values betweentwo locations of the image.

The image processor 706 may also be configured to cross-correlate imagedata between two or more images. For example, in a time series study, itmay be desirable to track a change at a location for a subject overtime. The processing protocol may identify which images to use (e.g.,which of the images in a sequence of images) and a comparison to performfor the image data. As another example, in a multi-modality study, twoor more types of image data may be collected. The image data may notreadily combine to provide an imaging result. A processing protocol maybe specified to indicate how, for example, MRI image data may becross-correlated with spectral image data.

The image processor 706 may use the position information identified bythe position detector 704 to process the image data. The image processor706 may identify the portion of the image data specified by theprocessing protocol based on the position information. Severalnon-limiting advantages of image data processing based on positioninformation are described in further detail below.

The image processing server 700 may be configured to guide thecollection of image data. The imaging controller 780 may generateconfiguration instructions to adjust an imaging device. The adjustmentmay allow the imaging device or sensor coupled therewith to accuratelycapture the image data needed for an experiment. For example, an imagemay be captured that shows the entire subject. However, the area ofinterest for the experiment may be the head. In such instances, theimaging controller 780 may receive a configuration request from theimaging device. The request may indicate the experiment, the imagingdevice, and a subject to be imaged. In some implementations, the requestmay include an image of the subject within the positioning assembly.Using the provided information, the imaging controller 780 may identifythe position of the subject to be imaged. Based on the position and thedesired anatomical feature to be imaged, the imaging controller 780 mayidentify an area of the subject where the imaging device will obtaindata. With the area determined, the imaging controller 780 may thengenerate a specific command to cause adjustment of the imaging devicefor capturing the desired image data for the identified area.

The memory 710 may contain computer program instructions that theprocessing unit 708 executes in order to implement one or moreembodiments. The memory 710 may include random access memory, read onlymemory, and/or other persistent, non-transitory computer readable media.In some implementations, the memory 710 may be implemented in whole orin part as a remote memory device (e.g., at a network location in datacommunication with the image processing server 700). The memory 710 canstore an operating system that provides computer program instructionsfor use by the processing unit 708 or other elements included in theimage processing server in the general administration and operation ofthe image processing server 700. The memory 710 can further includecomputer program instructions and other information for implementingaspects of the present disclosure.

FIG. 45 shows a message flow diagram for an example position based imageprocessing. For example, the message flow 800 shown in FIG. 45illustrates how image data may be received by an image processing serverto generate an image processing result. The message flow 800 shown inFIG. 45 provides a simplified view of messages that may be exchangedbetween the entities shown for position based image processing. It willbe understood that additional entities may mediate one or more of themessages shown in FIG. 45.

An image processing request message 805 may be transmitted from animaging device 604 to the image receiver 702 included in the imageprocessing server 700. The imaging device 604 may include a hardwareadapter configured to transmit the image processing request message. Theimage processing request message may include information identifying oneor more of: an experiment, a subject, the imaging device 604, images,text, audio, a reference to the image data (e.g., network location,uniform resource locator), a sensor used to obtain the image data,security/authorization information (e.g., a token), conditions at theimaging device 604 (e.g., temperature, humidity, pressure, light level,sound level), conditions of the imaging device 604 (e.g., resourcelevels (e.g., power, fluid or other experimental materials), time sincelast maintenance, available sensors, etc.), conditions of the sensorused to generate the image data (e.g., resource levels, time since lastmaintenance, capture settings), and the like. Collectively, thisinformation may be referred to as “image data.”

The image receiver 702 may determine the message 805 is an imageprocessing request message based on information included in the message805. The image receiver 702 may then send a message 810 to the imageprocessor 706 including the image data or a reference to the image datato be processed. The image processor 706 may provide a position requestmessage 815 to the position detector 704. The position request message815 may include all or a portion of the image data received from theimage receiver 702. In some implementations, the image processor 706 mayperform preliminary processing of the image data to extract a portion ofthe image data that identifies the positioning assembly used to capturethe image data. This may include identifying a portion of the image dataincluding a machine readable identifier for the positioning assembly.

Via messaging 820, using at least some of the information included inthe position request message 815, the position detector 704 identifiesthe position definition corresponding to the request 815. Theidentification may include querying the subject atlas data store 614 orother data storage device that includes a data record associating themachine readable identifier with a positioning assembly. In someimplementations, an image processing configuration may be provided tothe image processing server 700. The image processing configuration mayinclude the associations between the identifier included in the imagedata and the positioning assembly.

The identified position definition is then transmitted via a positiondefinition message 825 to the image processor 706. In someimplementations, the image processing server 700 may be processingmultiple images. In such implementations, it may be desirable to includeinformation to distinguish message flows for respective image data. Theposition definition message 825 may include an identifier to facilitatecoordinate of a response with a particular request. The identifier maybe specific to the overall image processing flow or to a portion of theprocessing flow such as the position request.

Via messaging 830, the image processor 706 generates an imaging result.The image processor 706 may generate the imaging result based on theposition definition received via the position definition message 825.The image processor 706 may be configured to identify a processingprotocol using one or more of the image data, the detected position, andthe position definition. As discussed above, the processing protocol mayidentify which values to use for generating the imaging result. Theprocessing protocol may identify how to combine or compare the valuesfor generating the imaging result. The processing protocol may identifya desired output for the imaging result (e.g., text, image, data plot,etc.). In some implementations, the processing protocol may identify oneor more destinations for the imaging result. For example, it may bedesirable to deliver the imaging result to an electronic communicationdevice associated with a researcher for the experiment. This may includegenerating and transmitting an email message, a text message, amultimedia messaging server message, a fax message, or other digitalcommunication for delivery to the identified destination(s).

As shown in FIG. 45, the image processor 706 provide an imaging resulttransmission message 835 to a destination, such as the destinationspecified in the processing protocol. In some implementations, theimaging result transmission message 835 may be stored for presentationin response to a request. For example, the imaging result transmissionmessage 835 may include a report. The report may be published and madeavailable through a web-server. A researcher using an electronic deviceconnected to a network may access the web-server and requestpresentation of the report.

FIG. 46 shows a process flow diagram for an example method of generatingan imaging result. The method 900 may be implemented in whole or in partby one or more of the devices described in this application such as theimage processing server 700 shown in FIG. 43 or FIG. 44. In someimplementations, the method 900 may be implemented on device thatincludes an integrated global memory shared by a plurality ofprogrammable compute units that includes a buffer, wherein the buffermay include a first-in-first-out (FIFO) buffer. The device may furtherinclude an integrated circuit (IC) that may include at least oneprocessor or processor circuit (e.g., a central processing unit (CPU))and/or an image processing unit (IPU), wherein the IPU may include oneor more programmable compute units.

At block 905, image data for an experiment is received. The image datamay be received from an imaging device, such as the imaging device 604.The image data may be received via wired, wireless, or hybridwired-wireless channels. The image data may be received by an image datareceiver, such as the image receiver 702. Receiving the image mayinclude receiving a machine-readable message including the image data.The image data may include an identifier for the experiment. Thisidentifier may be used to determine how the image data should beprocessed.

At block 910, a determination is made as to whether the experimentassociated with the image data is known. For example, a processingprotocol may be stored in a data storage device in communication withthe image processing server. In some implementations, the processingprotocol may be specified using the image processing serverconfiguration. The processing protocol may be stored in association withan identifier for the experiment. The determination at block 910 mayinclude accessing the data source for processing protocols to determinewhether the experiment has a defined processing protocol.

If the determination at block 910 is negative, at block 915, processingprotocol information for the experiment is received. The processingprotocol information may be included in a message. It may be desirableto include an identifier for the experiment along with the processingprotocol information in the message. The message may be received via auser interface (e.g., web-page, client application, etc.). Theprocessing protocol information may be received via wired, wireless, orhybrid wired-wireless channels. In some implementations, the processingprotocol information may be received via a protocol definition file. Theprotocol definition file may be a structured data file including theinformation specifying one or more of: which image data is used forprocessing, how the image data is combined or compared, output for theimaging result, and destination for the imaging result. At block 915,the received processing protocol information may be validated. Forexample, the type of image data included in the protocol (e.g., MRIinformation) may be unavailable from the identified image sources (e.g.,X-ray images). In such implementations, an indication of the invalidinformation may be provided.

Once a valid processing protocol is defined (e.g., block 915) for theexperiment, or previously defined (e.g., affirmative determination atblock 910), at block 920, the processing protocol for the image data forthe experiment is obtained. The processing protocol may be obtained froma data storage device configured to persist the processing protocolinformation. For example, the image processor 706 may query a data storeusing the experiment identifier for the processing protocol. In someimplementations, an experiment may be associated with multipleprocessing protocols. The respective protocols may be defined for imagetypes, subject types, or other variables within the experiment. In suchimplementations, the query may include the additional information toidentify the applicable processing protocol for the received image dataand experiment. In some implementations, it may be desirable to identifymultiple processing protocols for processing image data. For example, ageneral purpose data collection processing protocol may be associatedwith the experiment along with an image data type specific processingprotocol. In such implementations, the processing protocols may beassociated with a priority such that when multiple processing protocolsare identified for image data for an experiment, the order in which theprocessing protocols are executed can be determined based on thepriority (e.g., higher priority protocols are executed before lowerpriority protocols).

FIG. 47 shows an example of an analysis grid that may be associated witha processing protocol. The analysis grid 1000 may define a division foran image into discrete units. One unit 1002 is labeled in FIG. 47. Fortwo dimensional image data, the unit 1002 may represent a pixel. Forthree dimensional image data, the unit 1002 may represent a voxel. Theanalysis grid 1000 may further define areas of interest. The areas ofinterest define specific units from an image that will be used toanalyze the image. As shown in FIG. 47, a first area of interest 1012and a second area of interest 1104 are defined. It will be appreciatedthat an analysis grid may define only one area of interest or more thantwo areas of interest depending on the image analysis to be performed.

In some implementations, the processing protocol may indicate ananatomical feature of interest (e.g., liver). In such implementations,the anatomical feature of interest may be used to generate an analysisgrid. For example, a subject atlas may include modeling information forthe anatomy of a subject having certain attributes (e.g., strain,gender, age, etc.). The modeling information may include identifiersassociated with location information for specific anatomical featuressuch as named organs, vascular structures, skeletal structures, and thelike. Using the anatomical feature indicated in the processing protocoland a subject atlas, specific location information may be determined forthe feature within the subject.

Returning to FIG. 46, at block 922, a position of a subject shown in theimage data is determined. The position may be determined by providingall or a portion of the image data to a position detector, such as theposition detector 704. The identification may be based on informationindicating the positioning assembly used for capturing the image data.For example, an identifiable mark may be placed on a mold in which ananimal subject was placed to capture the image data.

Identifying the position may include identifying a position definitionfor the positioning assembly. Once the position definition isidentified, the position of the subject shown in the image data may beassessed by correlating the image data with the position definition. Thecorrelation may include aligning the position definition with the imagedata. For example, the position definition may include identification ofa head of the subject. The portion of the position definitioncorresponding to the head may be aligned with the portion of the imagedata showing the head. The alignment may include rotating the image data(e.g., 90 degrees, 180 degrees, 270 degrees), scaling the image data,transposing the image data, or the like.

FIG. 48 shows an example of an image of a subject that may be includedin the image data. The image 1100 includes an ordered group of pixels.The pixels include color information that cause representations ofshapes to be shown. The image 1100 shows a representation of a subject1102. The subject 1102 shown in the example of FIG. 48 is a mouse. Theimage 1100 also shows a representation of a positioning assemblyidentifier 1104. As shown in FIG. 48, the positioning assemblyidentifier 1104 includes a barcode and textual information. Thepositioning assembly identifier 1104 may be used to determine whichpositioning assembly was used for retaining the subject 1102 shown inthe image 1100.

Returning to FIG. 46, at block 922, a positioning assembly identifier,such as the positioning assembly identifier 1104, may be used todetermine the position of the subject.

At block 924, an imaging result is generated using the processingprotocol, the image data, and the position information. Generating theimaging result may include generating a graphical result, textualresult, data plot, or other output representing features identifiedusing the image data. The generating may include identifying a portionof the image data based on the processing protocol and the positioninformation. For example, the processing protocol may be defined toprocess color intensity at a specific location (e.g., brain) of thesubject shown in an image included in the image data. Generating theimage result may include parsing the image to include only the portionsincluding the feature of interest for further processing. This canreduce the amount of resources needed to process the image data as onlythe portions of the image data relevant to the processing protocol maybe processed.

Generating the imaging result may include determining the locationidentified in the processing protocol refers to a pixel location. Theimaging result may be generated using the pixel information at anidentified location in the first image. Generating the imaging resultmay include determining the location identified in the processingprotocol refers to a voxel location. The imaging result may be generatedusing the voxel information at an identified location in the firstimage. The processing protocol may indicate how the information at thespecified location is to be analyzed. For example the processingprotocol may indicate one or more of: thresholds for comparison, rangesfor comparison, or an equation with variables for representing arelationship for the information. In some implementations the processingprotocol may identify a remote service for obtaining the imaging result.For example, a researcher may provide a server with custom analyticalservice interface. The image data or identified information for aspecific location may be provided via this interface to obtain, inresponse, the imaging result.

FIG. 49 shows an overlay diagram for the example image of FIG. 48overlaid with the analysis grid of FIG. 47. As shown in FIG. 49, thefirst area of interest 1012 and the second area of interest 1014 arelocated within the image 1100. Using these areas of interest, a firstimage portion 1202 including image data for the first area of interest1012 may be identified and a second image portion 1204 including imagedata for the second area of interest 1014 may be identified. In someimplementations, the first image portion 1202 and the second imageportion 1204 may be used to generate smaller files for further imageprocessing.

The processing protocol may include configuration information toindicate how the data represented in the portions are to be analyzed.For example, using the first image portion 1202, if the metric ofinterest is color intensity, the processing protocol may direct theimage processor 706 to process each pixel included in the first imageportion 1202 to generate an average color intensity shown in the firstimage portion 1202.

As another example, using the first image portion 1202, where the image1100 is a three dimensional image, the metric of interest may be toidentify a total area having a minimum color intensity. In suchimplementations, the processing protocol may direct the image processorto count the number of voxels included in the first image portion 1202forming a contiguous area having the minimum color intensity.Furthermore, the mean, median, or maximum value of color intensity canbe determined for a region of interest.

A region-of-interest (ROI) can be automatically determined by thesystem, without any operator bias. For example, the ROI may bedetermined by calculating an average image of color intensities using apool of individual images with different color intensities. The spatialdistribution of color intensities may be transformed into a map ofintensity probabilities for each individual image. An intensityprobability map may describe the distribution of likelihood of theoccurrence of color intensity. For example, a large color intensityvalue may be directly correlated with a large intensity probability,whereas a small color intensity may be correlated with a small intensityprobability. It may be desirable to have the total sum of all intensityprobabilities of each individual image be “1.” The average intensityprobability map may be calculated by averaging the probability for eachpixel/voxel. In an average intensity probability map, it may bedesirable for the total sum of probability be “1.”

An ROI can be determined automatically by selecting pixels/voxels withthe largest intensity probabilities. For example, when processingbioluminescence image data, intensity probabilities can equate to thehighest probability of light emission of the image, and hence can beconsidered as an indicator for a ROI.

The method 900 generally describes how an imaging result may begenerated for image data based on the information included image data.In some implementations, it may be desirable to compare information fromimage data captured at different times and/or by different sensors. Forexample, an imaging device may include two sensors each configured todetect different properties for a subject. In such implementations,image data may be received from each sensor. As another example, anexperiment may wish to track efficacy of a drug over time or between acontrol subject and a subject exposed to a drug. An imaging resultgenerated from multiple, differing image data may generally be referredto as a cross-correlated imaging result.

FIG. 50 shows a process flow diagram for an example method of generatinga cross-correlated imaging result. The method 1300 may be implemented inwhole or in part by one or more of the devices described in thisapplication such as the image processing server 700 shown in FIG. 43 orFIG. 44. In some implementations, the method 1300 may be implemented ondevice that includes an integrated global memory shared by a pluralityof programmable compute units that includes a buffer, wherein the buffermay include a first-in-first-out (FIFO) buffer. The device may furtherinclude an integrated circuit (IC) that may include at least oneprocessor or processor circuit (e.g., a central processing unit (CPU))and/or an image processing unit (IPU), wherein the IPU may include oneor more programmable compute units.

At block 1305, first image data and second image data for an experimentis received. The first image data and second image data may be receivedin a single message or via separate messages. As discussed above, thefirst image data may differ from the second image data in one or moreways such as capture modality, capture time, subject imaged, position ofthe subject, or the like. The first image data and the second image datamay be received from the same imaging device or from two differentimaging devices. The image data may be received by an image datareceiver, such as the image receiver 702. Receiving the image mayinclude receiving a machine-readable message including the image data.The first image data and the second image data may each include anidentifier for the experiment. This identifier may be used to determinehow the respective image data should be processed.

At block 1310, a first quantitative result is generated for the firstimage data. Generating the first quantitative result may includecorrecting the light intensity for a portion of the first image data.For example, light intensity at a portion of the image data showing thetissue surface may be corrected. The correction may be based on lightexit angle relative to the imaging plane. In this way, light intensitieswith a large exit angle that appear to be smaller in the uncorrectedfirst image data, can be adjusted to provide a more accurate intensityvalue. At block 1315, a second quantitative result is generated for thesecond image data. The generating at block 1315 may be similar to thegenerating at block 1310. The first quantitative result may be generatedby processing the first image data using the method 900 shown in FIG.48.

At block 1320, a cross-correlated imaging result is generated. Thecross-correlated imaging result may be generated using the firstquantitative result, the second quantitative result, the processingprotocol for the experiment and, in some implementations, one or more ofthe first image data, the second image data, or information basedthereon (e.g., an attribute of the subject identified by the imagedata).

Generating the cross-correlated imaging result may include comparing thefirst quantitative result with the second quantitative result asdirected by the processing protocol. For example, the cross-correlationmay include presenting the respective image in a format that permitsparallel comparison of the different image data.

FIG. 51A shows a graphic diagram of an example presentation ofcross-correlated imaging result. In the cross-correlated imaging result1400 shown in FIG. 51A, four imaging results (1410 a, 1410 b, 1410 c,and 1410 d) captured at respective times (1415 a, 1415 b, 1415 c, and1415 d) are presented visually side-by-side.

FIG. 51B shows a graphic diagram of another example presentation of across-correlated imaging result. In the cross-correlated imaging result1450 shown in FIG. 51B, four imaging results (1460 a, 1460 b, 1460 c,and 1460 d) captured at respective times (1475 a, 1475 b, 1475 c, and1475 d) are presented visually side-by-side. The imaging results 1460 athrough 1460 d may be for the same subject shown in the imaging results1410 a through 1410 d of FIG. 51A. However, in FIG. 51B, the results areshown from a sagittal view while the results in FIG. 51A are shown froma coronal view. In some implementations, the original image data used togenerate the imaging results may not have included a depiction of one ormore of these views. Through image processing using the positioninformation for the image data and the subject atlas, such views can beconstructed. For example, views may be combined. One combination may beto merge a ventral and a dorsal view to generate in a single view aroundthe entire animal (e.g., 360 degree view). In such merged views, thelight intensities may be corrected such as to account for differentCosines of the exiting angle of the light.

FIG. 52 shows a plot diagram of a further example presentation of across-correlated imaging result. As shown in FIG. 52, imaging resultsfrom two different subjects are presented on a single cross-correlatedimaging result 1500. The imaging results for a first subject includecolony forming units over time for three anatomical structures of thefirst subject, namely left kidney (“Kidney L”), right kidney (“KidneyR”) and bladder (“Bladder”). The imaging results for a second subjectincludes colony forming units over time for three anatomical structuresfor the second subject, namely left kidney (“Kidney L naive”), rightkidney (“Kidney R naive”) and bladder (“Bladder naive”). As can be seenin the cross-correlated imaging result 1500, the second subject exhibitsa substantially lower number of colony forming units as compared to thefirst subject over time.

The image processing server 700 may generate other image processingresults. Examples of other image processing results that may begenerated for the image data using the positioning assembly and positiondefinitions described include 3D reconstructions, maximum intensityrepresentations, sagittal views, coronal views, transaxial slice views,interactive (e.g., rotatable) views, movies, and/or 3D views along anx-axis in time. The image processing server 700 may cause presentationof an interface for visualizing an imaging result. The interface mayinclude control elements that, upon activation, adjust therepresentation of the imaging results shown. For example one or morecontrol elements may be provided to: enable or disable data transparencyand select from a list of anatomical features of interest (e.g., organs)and 3D biolum of those organs will only show up. Because regions ofinterest may be predefined for all organs, the adjustment to shown orhide regions of interest involves displaying voxels in the region ofinterest and hiding voxels outside of the region of interest. Theinterface may provide an imaging result generated from the initialimaging result. For example, as data included in the result is filtered(e.g., by region(s) of interest), a filtered imaging result may begenerated that provides a representation that integrates values in theregion of interest. In some implementations these values for the regionof interest may be plotted as a function of x-axis in time or auser-defined database label.

In addition to providing interface features for a specific imagingresult, the interface may also include features for comparing multipleimaging results. For example, a control element may be provided toreceive information identifying another dataset to compare with theimaging result. In some implementations, the information may alsoinclude a desired analysis to use for comparing the selected datasetwith the imaging result. The control element may selectively presentanalytics that are available based on the imaging result and theselected dataset. For example, the analytics available may be limited bythe type of data or quantity of data in the selected dataset.

The interface may provide an analysis aggregate of all the selecteddatasets. For example, an aggregate may include a cross-correlation ofimage data collected for subjects undergoing different therapies.

An image processing server may also be configured to direct theacquisition of image data. For example, it may be desirable to provideinformation to an imaging device to configure the imaging device tocapture specific image data, such as for a specific experiment, subject,and/or position. Such features can increase the reliability of the imagedata received by ensuring the appropriate portions are captured at aspecified resolution, rate, format, etc. for processing via protocolsdefined for the experiment.

FIG. 53 shows a message flow diagram for an example process ofdynamically configuring an imaging device. For example, the message flow1600 shown in FIG. 53 illustrates how an imaging device can be adjustedfor image data capturing using an image processing server. The messageflow 1600 shown in FIG. 53 provides a simplified view of messages thatmay be exchanged between the entities shown for dynamically configuringan imaging device. It will be understood that additional entities maymediate one or more of the messages shown in FIG. 53.

The imaging device 604 may determine that a subject is ready forimaging. The readiness may be determined by detecting the insertion of apositioning assembly in an area near the sensor. The readiness may bedetermined by an input to the imaging device 604 such as via a button orinterface. The readiness may cause an imaging control request message1605 to be transmitted to the image processing server 700. The imagingcontrol request message 1605 may be routed to the imaging controller780.

The image control request message 1605 may include informationidentifying one or more of the subject to be imaged, the experiment, thepositioning assembly to be used during collection of the image data, theimaging device, and the sensor of the imaging device to be used forcollecting the image data. The imaging control request message 1605 mayinclude information identifying the message 1605 as an imaging controlrequest message thereby allowing the image processing server todistinguish between requests for image processing and requests forimaging control. In some implementations, the imaging control requestmessage 1605 may include an image (e.g., taken with a camera) of thesubject such as that shown in FIG. 48. Based on the

The imaging controller 780 may generate and transmit a position requestmessage 1610 to the position detector 704. The position request message1610 may include all or a portion of the data included in the imagecontrol request message 1605. In some implementations, the imagingcontroller 780 may cause preliminary processing of the image data toextract a portion of the image data that identifies the positioningassembly used to capture the image data. This may include identifyingimage data for a machine readable identifier for the positioningassembly.

Via messaging 1615, using at least some of the information included inthe position request message 1615, the position detector 704 identifiesthe position definition corresponding to the request 1615. Theidentification may include querying the subject atlas data store 614 orother data storage device that includes a data record associating themachine readable identifier with a positioning assembly. In someimplementations, an image processing configuration may be provided tothe image processing server 700. The image processing configuration mayinclude the associations between the identifier included in the imagedata and the positioning assembly.

The identified position definition is then transmitted via a positiondefinition message 1620 to the imaging controller 780. In someimplementations, the image processing server 700 may be processingmultiple control requests. In such implementations, it is desirable toinclude information to distinguish message flows for respective controlrequests. The position definition message 1620 may include an identifierto facilitate coordinate of a response with a particular request. Theidentifier may be specific to the overall control flow or to a portionof the control flow such as the position request.

Via messaging 1625, the imaging controller 780 generates an imagingdevice configuration command message. The imaging controller 780 maygenerate the imaging device configuration command message based on theposition definition received via the position definition message 1620.The imaging controller 780 may be configured to identify a processingprotocol for the anticipated image data for the experiment using one ormore of the image data, the detected position, and the positiondefinition. As discussed above, the processing protocol may identifywhich values to collect. The processing protocol may identify a desiredformat for the image data (e.g., JPG, GIF, TIFF, XML, comma separatedvalues, etc.). This can ensure that the image data collected by theimaging device 604 can be processed via the image processing server.

Generating the imaging device configuration command may also includeidentifying a location of interest to be imaged. For example, for thesubject in the identified pose and the imaging device that will becollecting the image data, it may be desirable to provide direction tothe imaging device as to where image data should be collected. Considera mouse subject undergoing an antibacterial drug experiment. Theexperiment may be testing efficacy of a drug to reduce bacterial countsin the mouse's liver. In such experiments, it may be desirable tocollect image data from the liver area of the mouse.

Using a subject atlas entry corresponding to the attributes of the mousewithin the identified position, a location of the liver area can beidentified. This location information can then be used to generate aconfiguration command for the imaging device such that the area ofinterest is detected.

This can provide several non-limiting advantages. One non-limitingadvantage is assured capturing of image data for the area of interest.Rather than obtaining image data and then confirming whether the imagedata is suitable for the experiment, the imaging device can beconfigured dynamically, before capturing the image data, to ensure thedesired data is obtained. As another example of a non-limitingadvantage, by identifying the specific anatomical region for imaging,the imaging device can hone in on an area of interest rather thanwasting resources capturing areas that are not of interest. Furthermore,for some imaging devices, the sensor may cause adverse effects suchthose caused by exposure to excessive radiation. As another example, bytargeting a specific anatomical region, the resolution obtained for theimage data may be increased because the sensor data can be concentratedat a specific area of interest rather than spread out over the entiresubject. As another example, the capturing of image data for a specificarea can provide a consistent frame of reference for cross-correlationstudies. In an experimental study where the image data is not capturedin a targeted manner, an initial selection is typically performed toidentify the area of interest included in the image data. This can besubjective and prone to error. Furthermore, the surrounding area maybias or otherwise affect the analysis of the actual area of interest. Bytargeting at the time of data collection these sources of experimentalerror can be reduced.

Another non-limiting advantage of using an organ probability map (OPM)is the automatic registration of the subject's anatomy to the opticalsignal. Hence, specific regions of the subject can be analyzed accordingto its known anatomical structures, instead of ‘guessing’ where thesignal is coming from. Furthermore, a so-called ‘biodistribution’ of theoptical signal may be obtained. The biodistribution increases theaccuracy for determining the optical light intensity distributionbecause the intensity is identified and can be corrected according toits anatomical source of origin rather than a static coordinate locationof an image. This accounts for minor anatomical variations that can havesignificant impact on the detected image data.

The imaging controller 780 may also consider previously generatedimaging results when generating the configuration command. For example,if a subject identified in the image control request message 1605 isassociated with a prior imaging result, additional information about thesubject, its anatomy, trajectory through the experiment, etc. may beknow. Using one or more of these elements of prior information, theconfiguration command can be further tailored to the specific subject.For example, while subjects are selected from a known strain of mice,there is some chance that small variations exist between mice. The liverfor a given mouse could be located in a different location than anothermouse. The configuration command can be adjusted to account for suchvariations. Specifically, if the location specified is for a standardlocation of a liver in a mouse, the location information can be adjustedbased on the prior identification of the liver in a specific mouse.

The imaging device configuration command may be generated using alibrary of commands. The library of commands may specify specificmessage formats and directives that can be specified for a specificimaging device. The imaging controller 780 may use the informationincluded in the imaging control request message 1605 to identify theimaging device 604 and select, such as from the library of commands, oneor more commands to configure the imaging device 604.

The imaging controller 780 may then transmit a configuration commandmessage 1630 including the identified information for capturing imagedata for the specified subject. Via messaging 1635, the imaging device604 may be adjusted using the configuration command message 1630.Adjusting the imaging device 604 may include adjusting a sensorlocation, adjusting a sensor emission, adjusting a sensor detector,adjusting a duration of image data capture, adjusting a format for imagedata captured, or other operational attribute of the imaging device 604or sensor associated therewith.

FIG. 54 shows a process flow diagram for another example method ofgenerating a cross-correlated imaging result. The method 1700 may beimplemented in whole or in part by one or more of the devices describedin this application such as the image processing server 700 shown inFIG. 43 or FIG. 44. In some implementations, the method 1700 may beimplemented on device that includes an integrated global memory sharedby a plurality of programmable compute units that includes a buffer,wherein the buffer may include a first-in-first-out (FIFO) buffer. Thedevice may further include an integrated circuit (IC) that may includeat least one processor or processor circuit (e.g., a central processingunit (CPU)) and/or an image processing unit (IPU), wherein the IPU mayinclude one or more programmable compute units.

The method 1700 shown in FIG. 54 describes a data flow for generating across-correlated imaging result, given a set of preclinical images ofsmall animals as input and statistical and quantitative informationabout the biological target as output. The method 1700 may beimplemented as web application accessible by/in a web browser. Themethod 1700 may utilize a connection between the client and a cloudserver for data storage and data analysis.

The method 1700 begins by receiving an image upload 1702 and annotationinformation 1704 for the image upload 1702. The image upload 1702 mayinclude an image of a subject captured in a specific position. The imageupload 1702 may include a set of preclinical images obtained withoptical, CT, MRI, PET, SPECT, and/or nuclear imaging systems. The imagesmay be uploaded to a central cloud server. These images included in theimage upload 1702 may can have a propriety format or other standardformats like TIFF, JPEG, GIF, DICOM, etc.

Uploaded images can be displayed via a user interface such as webapplication accessible via a web browser at a client site. The userinterface may include control elements to receive the annotationinformation 1704 for one or more of the images included in the imageupload 1702. For example, a client may select or enter text informationindicating the subject ID, the sex, the weight, and the study cohort ofthe subject shown in an image.

After receiving the annotation information 1704, at block 1706, theimages may be preprocessed to generate a data set for further dataanalysis. The generated data set may include a unique standard or formatand can be used interchangeably across different images, cohorts,subjects, or studies. For example, a set of images pixels may be croppedfrom the raw images and scaled to a uniform size with uniform data setsize. The cropping may be performed based on one or more characteristicsof the subject such as the weight or size of the subject. Thepreprocessing at block 1706 may include storing the generated data setin the cloud storage in association with the image upload 1702.

At block 1708, a virtual detector point, with predefined spatialcoordinates (x,y,z), may be assigned to each data point (I) of thepreprocessed images. When assigning the virtual detector points, it maybe desirable to ensure that all processed images in a data set have thesame or similar number of detector points and with same or similarrelative spatial locations for each virtual detector point. A virtualdetector point may include specific information identifying the imagingmodality used to capture the image, such as the light intensity, x-rayabsorption parameter (Hounsfield unit), or percent injected dose orradionuclide activity. Where the virtual detector points share a mutualcoordinate system across different animals/subjects, cohorts, imagingmodalities, and study points, the preprocessed images are ready forcross-comparison. Subsequently, the detector data points may bedisplayed as images in the user interface (e.g., the web browser) foreach subject and time point.

At block 1710, the user interface may receive selection of aregion-of-interest (ROI). The selection may include selecting a set ofthe virtual detector points. The ROI may be determined automatically byreceiving a selection (e.g., from a menu of options include on the userinterface) specifying an organ or issue region, or by manually selectiona ROI from the spatial distribution of detector data points. The spatialcoordinates of an organ may be provided by an organ probability map(OPM), which shares the same spatial coordinates system as the virtualdetector data points. The selected ROIs may be transmitted to a cloudserver for data analysis across different subjects and/or time point(s).

At block 1712, the ROI analysis may be performed automatically on thecloud server. The analysis may be performed for each subject. Theanalysis may include generating one or more of the mean, median,maximum, or total value inside the ROI. These data may provide a basiclayer for further data analysis performed at different levels, includingat the single animal level, the cohort level, the study level, orbetween different studies. The result of the ROI analysis may include astudy data model 1714.

The data analysis, using the ROI information as input, can includedifferent statistical methods or other methods for comparing orquantifying data sets. The data analysis can be applied to a data set ofa single animal by comparing different virtual detector points atdifferent spatial locations or time points. It can also be applied to aset of animals within a single cohort, or to different cohorts 1716within a single study. Furthermore, different studies can be compared toeach other.

The statistical methods can include linear fitting or regressionanalysis of data points 1722 included in the study data model 1714and/or cohorts 1716, or more advanced techniques 1724 such as Student'st-test, k-means clustering, analysis of variance (ANOVA), or singularvalue decomposition (SVD).

At block 1718, the final results may be exported to a local storagemedium. At block 1720, the final results may additionally oralternatively be plotted on a user interface for presentation such asvia the web browser.

The described aspects of the cross-correlation imaging system can beflexibly implemented in a variety of embodiments. In one implementation,the system may implement at least some of the described features as aplatform-based technology. The system may include integrated hardwareand software add-on and/or retrofit for imaging devices (e.g., BLIsystems) that is connected to a cloud-based image processing system. Theimaging device hardware plugin can enable quantitative 3D imaging ofbioluminescence cells and bacteria inside a living animal. Included insuch implementations may be (i) a body-shape conforming animal mold,(ii) a digital mouse atlas providing a dependable anatomical referenceand the non-uniform optical tissue parameter distributions, and (iii) anin vitro optical calibrator. Such implementations may be configured tomap the spatial bacterial density (CFU/mm³) distribution in a livingsmall animal and co-locate it to an anatomical reference provided by thedigital mouse atlas.

The cloud-based image data processing system may be connected to theplugin-unit and configured to collect image data, determine the 3Dspatial bioluminescent bacteria distribution, perform the datacalibration using the in vitro optical calibrator, and automaticallygenerate imaging results across animals and/or modalities withoutrequiring any operator interference. Some implementations may alsogenerate a final study report that provides imaging results such as across-correlated longitudinal study report. The image data processingsystem may be configured to provide automated data processing acrossdifferent animals and time points based on the animals' positions. Thepositioning assembly (e.g., the animal mold) may be included to providea constant spatial frame for all animals. Therefore, the need formanually drawing region-of-interests for each animal and thelabor-extensive book-keeping of data, while being prone to human errorof visual inspection, becomes obsolete. The features may be included inan unattended workflow such that automated image data processing andreporting of animal studies becomes feasible.

The imaging processing server may be located on a centralized server(“cloud”) and connected with a client computer for data acquisition(“imaging computer” that hosts the add-on/retrofit plugin unit). In someimplementations, the imaging processing server may be connected directlywith the imaging device. In some implementations, the image dataprocessing server may be in data communication with a client computer ofthe investigator. The image data processing server may be a speciallyarchitected computing system including specific hardware to facilitateefficient application of different mathematical tools for large-scaledata processing and storage across different animal and time points(“big data”).

In one implementation including some of the features described, abacterial density distribution inside a subject can be generated as theimaging result. To generate the bacterial density, the system mayinclude a single body-shape conforming animal mold for a 23 g mouse, anin vitro optical calibrator, an OPM for a male C3H mouse (23 g), and atwo-mirror gantry for multi-view imaging. These features may be used forexperiments where quantitative and biologically relevant information(e.g., CFU/mm³) about bacterial organ burden in place of physicalquantities of a bioluminescent source (photons s⁻¹ cm⁻³) can be obtainedin part using an imaging device and an OPM.

The animals may be immobilized inside the mold and placed on the mirrorgantry. A series of spectral images (e.g., four, three, six, or more)were taken from the dorsal/ventral side of each animal. The spectralimages may be taken at different wavelengths (e.g., 550-720 nm, 50 nmbandwidth, 3 min). The availability of wavelengths may be determinedbased on the number of optical filters included in the detector and/orthe spectral bandwidth of the filters included in the detector.Implementations may include 2 to 100 optical filters. A filter may havea spectral bandwidth of 1 nm-100 nm spectral. The spatial photonemission density distribution (photons s⁻¹ cm⁻³) may then bereconstructed with a simplified spherical harmonics (SP_(N)) based on,for example, BLT reconstruction. A calibration factor may then begenerated using the optical calibrator. Here, the photon emissiondensity distribution may be reconstructed by the BLT algorithm while theactual CFU count inside the calibrator was known. Therefore, the unknownbacterial density distribution (CFU/mm³) of one or more subjects couldbe determined by multiplying its reconstructed photon emission densityby the calibration factor determined from the optical calibrator. Thespatial CFU/mm³ distribution may then be co-registered to the OPM.

In an experimental system configured as described, a Pearson correlationcoefficient of R²=0.98 of the reconstructed with respect to the giventotal CFU inside the capsule was obtained. In comparison, planar BLI wasobserved as achieving an R²=0.48 when using the maximum light intensityof the region-of-interest. These preliminary data provide a proof ofconcept of calculating the bacterial density distribution inside aliving animal using a positioning assembly such as an animal mold, a BLTalgorithm, and an optical calibrator.

As part of the experimental system, a digital OPM mouse atlas wasdeveloped, which provides (1) an anatomical reference for the spatial invivo bacterial density distribution and (2) the non-uniform opticaltissue parameter distribution of μ_(a) and μ_(s)′ for the BLT algorithm.

In the experimental system, the OPM was built from contrast-enhanced CTscans using a vascular imaging agent such as AuroVist™ 1.9 nmcommercially offered by Nanoprobes, Inc. N=20 CH3/HeN mice with averageweight of 23±1 gram (˜10 wk) were imaged inside the mold with a micro-CT(such as a preclinical imaging system offered commercially by MedisoUSA), and several organs (e.g., skeleton, lung, kidneys, liver, heart,bladder, brain) were manually segmented using a medical imagingworkstation configured for segmentation such as with Mimics©commercially offered by Materialise NV. The segmented images weredefined on a Cartesian grid with 0.5 mm resolution and each organ islabeled with a numerical identifier. The OPM was generated bydetermining the probability p_(i) ^(j) (0≤p≤1) at each image voxel i forfinding a given organ j. The highest (lowest) probability p_(i) ^(j) of(not) finding a given organ j at voxel i is p_(i) ^(j)=1 (p_(i) ^(j)=0).The maximum probability, max (p_(i) ^(j)), of all j at given mutualvoxel determines a non-overlapping boundary between different organs.

To address the different optical properties of organs, the OPM may betranslated into a non-uniform map of the tissue absorption (μ_(a)) andreduced scattering (μ_(s)′) coefficients. The spectrally-dependent mapsof μ_(a) and μ_(s)′ can be built from the OPM for four partiallyoverlapping wavelength intervals of 50 nm between 550-720 nm (spectralrange with largest variation of tissue light absorption). In embodimentswere a different number of spectral images are used, the number ofoverlapping intervals would correspond to the number of spectral images.

Each voxel element i of these maps constituted the expectation value,μ_(i) , given the p^(j) and the μ_(d) ^(j) and μ_(s)′^(j) of each organ:μ_(i) =Σ_(i=1) ^(i)p_(i) ^(j)μ_(i) ^(j). The μ_(d) ^(j) and μ_(s)′^(j)coefficients were defined by the blood oxygenation level and byMie-Scattering theory. The ‘intermittent’ or ‘background’ tissue that isnot defined by a given organ were a mixture of muscle and fat.

The OPM atlas (R_(A)) generated by the experimental system was validatedwith additional segmented CT scans (R_(E)) of N=10 C3H/HeN mice (23 g).The accuracy of the atlas R_(A) was validated by voxel-wise comparingthe organ probabilities with largest p^(j) to the expert data set R_(E).DICE and volume recovery coefficients (VRC) were calculated between 0.9and 1.0. The DICE coefficient is the registration accuracy for eachorgan and is given by:

$\begin{matrix}{{DICE} = {2\; {\frac{R_{A}\bigcap R_{E}}{R_{A} + R_{E}}.}}} & (24)\end{matrix}$

The VRC is the ratio of the recovered organ volume of data sets R_(A)and R_(E). VRC and DICE coefficients≈1 indicate an exact match (0%error). Results were also compared to the outcome of similar mouse atlasstudies (21, 24, 25, 33). The root-mean-square-error (RMSE in [%]) isused for validating the milestone. The experimental system including thefeatures described yielded a DICE>0.6 (RMSE<40%) and 0.7<VRC<1.3(℄RMSE|<30%) for at least five out of seven organs.

One benefit of the describe image analysis features is the ability togenerate imaging results in real time. Real time may generally refer togenerating an imaging result in proximate time to when the image data isreceived for analysis. In some implementations, real time result mayrefer to a result generated from 1 second up to 1 minute after receiptof the image data.

One experimental system including the features described was configuredto generate an imaging result identifying the in vivo CFU/mm³distribution inside a urinary tract infection model using bioluminescentpathogenic E. coli. The spatial CFU distribution was registered to ananatomical reference.

An optical in vitro calibrator (2×3×10 cm³) was developed that consistsof four compartments of optical tissue phantom material (e.g.,polyurethane commercially available from INTO, Quebec) with differentμ_(a) and μ′_(s) at 600 nm (μ_(a)=0.14-1.24 cm⁻¹; μ′_(s)=10.5-12.5 cm⁻¹,Δμ=±10%). Hence, each compartment mimics the average optical propertiesof animal tissue at a single spectral band (50 nm) between 550 nm-720 nmof the bioluminescence spectrum. Each compartment has a cavity (80 μL),which can hold luminescent bacteria with known CFU/mm³. The lightintensity was measured at the calibrator surface by taking a singleimage at 600 nm. Given the different optical properties of eachcompartment at 600 nm, a data set of attenuated light images couldautomatically be assembled that is equivalent to the intensities of fourdifferent spectral bands btw. 550 and 720 nm. The BLT algorithmreconstructed the photon emission density and, given the known CFU/mm³inside the calibrator, the calibration factor for the photon emissiondensity could be calculated. This calibration factor was used later forthe in vivo experiments.

The feasibility of generating an in vivo CFU imaging result using theexperimental system was demonstrated by using an established model of aUTI by inoculating both wild-type C3H/HeN mice (N=10) which get cystitis(bladder infection), and C3H/HeJ mice (N=10) which get both cystitis andpyelonephritis (kidney infection) with small volumes of the lux-bacteria(20 μl of 5×10⁸ CFU ml⁻¹) by transurethral catheterization. The in vivobacterial burden in the bladders and the kidneys was determined 24 hoursafter inoculation using the calibrator. The animals underwent BLI andfour spectral images were measured. The BLT algorithm reconstructed thephoton emission density, which was transformed into the CFU count byusing the calibration factor. The spatial CFU distribution wasco-registered to the OPM mouse atlas and the organ site of bacterialinfection was instantaneously determined. Post imaging, the animals weresacrificed and the kidney and bladder volumes were determined and platedfor ex vivo CFU count. The total calculated CFU count was compared tothe total CFU count of harvested organs.

The image data processing was performed for all BLT reconstructionscalculated by the SP₇ model using g=0.7, g=0.9, and g=0.98, and thediffusion model for the same μ′_(s). The outcome of study series wascompared and the best method was determined. The measure of performanceused was the root-mean-square error (RMSE) and the Pearson correlationcoefficient (R²). First, the RMSE and R² of the calculated versus theactual total CFU of each organ was determined and compared. Next, theRMSE and R² was compared by either using (i) the ‘correct’ non-uniformoptical parameter distribution based on the OPM or (ii) the uniformoptical tissue properties (same parameters as from optical calibrator).Last, the R² of the reconstructed in vivo CFU count were compared withthe R² obtained from region-of-interest of 2D bioluminescence images(‘standard of practice’).

In one embodiment, the experimental system provided results with aRMSE<30% and a R²>0.94 in at least 7 out of 10 animals thus indicatingthat real time generation of the imaging results is achieved.

The cross-correlation image data processing is dependent, in part, onthe positioning of the subject. The positioning provides a way toprocess the image data using the position definition from a subjectatlas. A positioning assembly, such as an animal mold, can provide aconsistent surface for detector locations and allows for stablesource-detector geometries on which the kernel matrix is pre-determinedand hard-coded rather than built for each data set, affording anenormous computational cost savings. Given the consistent volumetricparameter space that the positioning assembly provides, similaritymeasurements between reconstructions of different mice and differenttime points can be directly computed due to the shared 3D Cartesian gridof the positioning assembly data sets and organ probability map. Thespatial domain being standardized by the positioning assembly, affineand morphological transformation calculations are not required, as wouldbe necessary in positioning assembly-free mouse imaging where volumetricgrids differ.

Similarity metrics of 3D reconstruction in the described imaging systemscan then be used to automatically classify animal cohort sets and othersets such as disease/therapy progression state using objective andunbiased cluster analysis. Localizing the site of bacterial infection inmice of different weight/size/age requires a body-weight-dependentanimal mold. Furthermore, a size-specific OPM for male and female miceis necessary for body-weight-dependent anatomical co-registration. Abody-weight-dependent animal mold also enables the automaticcross-correlation of image data across mice of different weight, sex,and strain and at different time points. The construction of an opticaltissue property map for the BLT analysis may use information about theanimal's anatomy for different animal ages and sex. X-ray CT and MRIhave been partially successful in providing anatomical information, butcan be expensive, and require additional imaging time, animal handling,and trained personnel. Low-cost non-tomographic methods, e.g.,photographic imaging, 3D surface scanning, and planar X-ray imaging havebeen considered for body profiling, but they alone do not provideaccurate anatomical information.

In experiments using mice, positioning assemblies may be provided fordifferent body weights of the C3H and BL6 mouse strain. The BL6 mousestrain has a slightly different body shape. It may be desirable to havethe positioning assembly be optically transparent to allow imagingdevices to detect the subject contained therein. The positioningassembly may hold the subject (e.g., animal) in a fixed position anddefine the subject's surface geometry. The positioning assembly mayconsist of two shell-like parts made of polycarbonate that can betightly secured with a latch, while slightly compressing the subject andwithout changing the animal's shape. The inner surface of thepositioning assembly may form the shape of a CH3/HeN mouse. Thepositioning assembly may fit into a mirror gantry inside an imagingdevice such as those commercially available from the IVIS™ (e.g., IVISSPECTRUM, IVIS 100, IVIS 200, IVIS LUMINA) commercially available fromPerkinElmer and/or the PhotonImager™ commercially available fromBiospace Lab.

A set of positioning assemblies may be provided to cover the range ofsubjects for an experiment. For example, in the case of an experimentusing mice, twelve positioning assemblies may be provided for covering aweight range of animals between 16.5-34.5 g with each positioningassembly accepting a weight tolerance of ±0.75 g. Assuming a maximumspatial tolerance of σ=0.375 mm between the animal's surface and thepositioning assembly is accepted (which is significantly less than themaximum achievable image resolution of 1-2 mm in BLT), a maximumtolerance of in mouse volume may be defined such as 1.5 cm³. In someimplementations, increasing the spatial dimensions along all three majorsymmetry axis of an animal by 2σ=0.75 mm, may yield an increase ofvolume of ≈1.5 cm³. The volume tolerance defines the weight change of1.5 g between two positioning assemblies, assuming a tissue density ofapproximately 1 g/cm³ In some implementations, an acceptable animalweight for a single BCAM may ranges between +/−0.75 g.

Each positioning assembly may be scaled according to an x,y,z-dependentscaling factor using one positioning assembly, such as the positioningassembly for a 23 g subject, as master copy. The scaling factors may bedetermined using optical imaging of the surface geometry of differentanimal sizes and validated with CT scans.

Positioning assemblies for different strains and body weights can berapidly be manufactured. For example, optically transparentpolycarbonate shells can be produced with an injection molding systemsuch as QuickParts which is commerically available from 3D Systems, Inc.The injection molds for generating the positioning assemblies may beformed of aluminum and can be machined using a CAD file of the scaledmold. In some implementations, a CAD file for a master copy may bescaled using the parameters described above to automatically generateadditional CAD files for different positioning assemblies within the setof positioning assemblies for a species.

OPMs for each sex (male/female) of different mouse breeds (C3H andC57BL6) may be similarly developed. For example, n=24 contrast-enhancedCT scans may be obtained for a female C3H mouse with body weight of 23g. In addition, n=24 CT scans of a male BL6 mouse (23 g) and n=24 scansof a female BL6 mouse (23 g) may be obtained. The OPM may also beexpanded to include identification of additional anatomical featuressuch as gallbladder, spleen, testicles, GI tract.

Referring now to the image processing server, commercial 3Dbioluminescence reconstructions are prone to over 50% variability inreconstruction intensity and distribution width, due touser-subjectivity in image data processing. Some current commerciallyavailable bioluminescence tomography implementations require tedious andsubjective user-input for multiple steps of the tomographic process, andgives rise to inconsistent reconstructions with the same data set.Longitudinal study evaluation with error-prone reconstructions inflatesinaccuracies. Regions- or volumes-of-interest analysis is vital forquantifying bacterial burden, proliferation, and therapeutic efficacy.Furthermore, regions- or volumes-of-interest may not be rigorous ifreconstruction noise properties are not known. Automating thereconstruction process using explicit noise analysis from the data-endto reconstruction-end provides further non-limiting advantages ofeliminating operator interpretation, enhancing data accuracy, increasingreproducibility, and reducing image data processing variability.

The image data processing process is possible, in part, through the useof standardizing positioning assemblies, which also serves to enclose acommon parameterized space in which mathematical operations can beperformed to automatically quantitatively characterize andsystematically compare reconstruction data. The image processing serverprovides an operator-independent analytical system for processingoptical image data. This cloud-based discovery process can lower costsand shorten study analysis time.

Operator-involved image data processing introduces subjectivevariability in 3D reconstructions and image interpretation. Quantitativeassessment of image quality is generally missing in commercial imagingmodalities. The statistical estimation underpinning of bioluminescentreconstruction may not effectively be conveyed to investigators andreconstruction interpretation thus can have a strong subjective element.Non-uniform spatial resolution and reconstruction artifacts may beinherent in 3D optical tomography and can be rooted in the mathematicalexpressions of the physical process. However, tomographic resolutionanalysis can expose resolution artifacts. Resolution analysis to uncoverartifacts must be considered. As such, quantitative 3D reconstructionresolution metrics can be used to make informed and respectableevaluations of disease temporal and spatial distribution, yet have notbeen sufficiently addressed for bioluminescence tomography.

Some image processing systems include features to identify artifactregions and determine resolution by determining uncertainty in thereconstruction parameters. Automation of 3D reconstruction resolutionmetrics enhances interpretability of the reconstructions, which can bedesirable for robustness and a transparent discovery process. Moreover,it is often a subjective exercise for an investigator to determine theextent of the region- or volume-of-interest boundaries by visuallyinspecting a 3D bioluminescence reconstruction. Classification schemesseparating feature detection (e.g., quantified bacterial distribution)from artifacts can be automated with use of reconstruction performancemetrics. Computer vision techniques may be included in the imageprocessing system to remove operator bias in feature extraction in thepresence of image noise and artifacts. Feature extraction algorithmsusing reconstruction performance metrics can segmentregions-/volumes-of-interest containing high quality data. Quantitativedata quality metrics provide more actionable and standardized data.Bacterial distribution reconstructions that have crossed the performancemetrics pipeline can be digitally projected onto an organ probabilitymap for automatic region of interest segmentation. This can replacetime-consuming organ harvesting and plating procedures and provideconsistent data on the temporal evolution of disease or therapy in thesame mouse.

Regions-of-interest manually drawn to compare 3D bioluminescentreconstructions between mice and time points can be prone toinconsistent and subjective selection by the operator. Using thedescribed features, similarity measurements between reconstructions ofdifferent subjects and different time points can be directly computeddue to the shared 3D Cartesian grid of the positioning assembly and theorgan probability map. Similarity metrics of 3D reconstruction imageresults can then be used to automatically classify blinded animal cohortsets and other sets such as disease/therapy progression state usingobjective and unbiased cluster analysis.

Characterization of the disease into anatomical categories can befollowed temporally in a quantified and automatic methodology.Automation for temporal quantification is achievable, in part, byexploiting the normalization which the positioning assembly and theorgan probability map that produces a consistent surface and referenceanatomy, as well as computer aided detection. This method offersindependence from user subjectivity, enhanced data accuracy, andincreases analysis speed by automating the full data analysis process.

Parallelized processing of the analysis, from reconstruction toanalytics, can further provide time and cost savings for bothcomputationally expensive algorithms and analysis of big data ensembles.Transparent, automated study reports generated by the cloud-basedsoftware can unburden researchers from screen-time and acceleratescientific discovery.

The image processing server may provide an imaging result usingreconstruction data which passes through the following analysispipeline: reconstruction uncertainty mapping, distinguish features fromartifacts, determine regions/volumes of interest, digitally plate andmeasure organs, and classify animals into cohort categories in a blindfashion.

For uncertainty mapping, the performance of the reconstruction algorithmcan be evaluated with quantitative performance metrics that will becomputed for each reconstruction data set. Tomographic sensitivity 3Dmaps may be dependent on imaging geometry, instrument sensitivity, andphysical models. Reconstruction resolution can be examined and optimizedby identifying source-detector geometries for each positioning assemblysize. Computer aided detection can automate feature selection (e.g.,anatomical feature of interest) in the reconstructions and remove usersubjective bias. The organ probability map can be used to associate thereconstruction features to anatomical sources in a statisticallyaccurate fashion. The association may be possible even when structuralimaging modality data is absent. For example, mapping of bioluminescentfeatures to anatomical organs can be used to categorize disease extent.

The quantitative performance metrics can be generated with thesimplified spherical harmonic model on a 3D Cartesian grid within adigitized animal mold surface. Fisher information

$( {{I(x)} = {- {E\lbrack {\frac{\partial^{2}}{\partial x^{2}}\ln \; {p( y \middle| x )}} \rbrack}}} )$

can be calculated to elucidate spatial resolution strengths, where E[⋅]is the estimator, x is the estimated parameter, y is the data, andp(y|x) is the probability distribution of y given x. Propagating datanoise through Cramer-Rao Lower Bound (CRLB≥1(x)⁻¹ for biased estimators)may be used to identify voxel lower bounds on intensity covarianceestimates, based on both model and data uncertainties. Tests of 3Dresolution can be conducted with simulated checkerboard source tests todetermine resolution matrices and local impulse response functions andcontrast-detail maps.

To distinguish features from artifacts, spatially distributedreconstruction image noise determined from parameter uncertaintyestimates as described above can be used to explicitly identify featurevoxels and enhance signal from noise in the reconstructed images usingan objective mathematical observer such as a channelized Hotellingobserver (CHO), which is based on reconstruction covariance. The CHOmetric may represent an image of signal-to-noise, identifying highbioluminescent signal areas for automatic ROI generation. A channelimpulse response filter, such as FWHM=[0.5,1.0,2.0] mm, may be providedfor optimum closed form area under the receiver operating curve (AUC).

As discussed above, the features described may provide a virtualrepresentation of the anatomical feature of interest, thereby avoidingthe need to perform physical organ harvesting. This may be referred toas digital harvesting and plating. To provide digital harvesting andplating, an organ probability map identified for each positioningassembly associated with image data can provide quantitative anatomicalreference to the localized bioluminescent sources. The organ probabilitymap may identify in 3D space the probabilistic location of organs,π_(i)(

)=[0,1], where i is the organ index at location {right arrow over (r)}in 3D voxel space. Bioluminescent intensity estimates x({right arrowover (r)})±σ_(x)({right arrow over (r)}) can be combined with the OPMfor each positioning assembly, so that at location {right arrow over(r)}, the intensity estimate may be generated using x_(i)({right arrowover (r)})±√{square root over ((σ_(x)({right arrow over(r)}))²+(x({right arrow over (r)})π_(i)({right arrow over (r)}))²)},associated with organ i if and only if π_(i)({right arrow over (r)})>0.

Having generated an imaging result, the system may further provideblinded cohort clustering. For systematic and unbiased data inquiry,automatic data set clustering to separate and identify cohorts withinlarge numbers of blinded data sets may be provided. The clustering mayinclude using similarity analysis with Figures of Merit (FOM) such ascross-correlation and mutual information between positioning assemblyvoxel populations. For example, two voxel box filter sizes=[3,5] may betested for separation skill of cross-correlation and voxel binsizes=[1,2] will be tested for mutual Information. The positioningassembly surface invariance can offer robust quantitative comparisonsbetween time points and animal weights on the same animal, and acrossdifferent animals in the cohorts. Spectral decomposition methods in thespatial and temporal domain, as well as classification algorithms, suchas self-organizing maps or k-means clustering using the above FOMs asdistance metrics, can be used for characterizing anatomical infectionpatterns with pathological condition.

The image processing service may be further configured to providekinematic monitoring image results. For example, time series analysis ofcluster evolution can be explicitly plotted, or derived from dimensionaldecomposition (factor analysis, principle component analysis) of thespatio-temporal data to indicate kinematics of infection migration andtherapeutic intervention disease reduction/prevention. 2D and 4D plotscan be generated for temporal monitoring. The time evolution of the 3Destimated distribution of disease will be displayed in 4D (the fourdimensions being x, y, z spatial locations and time), and as well as thetime evolution of disease identified to specific organs.

The features described may be implemented in or as part of anoperator-independent optical imaging platform system to monitor andquantify both planar (2D) and multispectral bioluminescence tomography(BLT/3D) co-registered to a novel digital mouse atlas. The featuresprovide a non-limiting advantage of permitting the automaticquantification and co-registration of a luminescent signal to an organ.This embodiment may include three parts: a body-conforming animal mold(BCAM); an organ probability map (OPM); and an in vitro opticalcalibrator (IVOC) device. Together the system permits the automatedmapping and quantification of light signal to the OPM.

The BCAM allows the subject (e.g., a mouse) to be placed in areproducible geometrically constrained position. This stereotactic frameallows repeatable animal positioning. This subject alignment systempermits high-resolution 2D data analysis and quantitative BLT which maybe co-registered to a novel anatomical atlas. Overall, the BCAM placesthe subject in a consistent position, regardless of its size, to monitorthe same spatial location (e.g., 1 mm×1 mm voxel) overtime. Digitallyembedded in the BCAM, is the organ probability map (OPM), representing adigital anatomical atlas. The OPM may represent the spatial probabilityof an organ in a particular voxel within the BCAM. The OPM permits“digital dissection” and OPM data can be used for longitudinalassessment of diseases. Furthermore, the OPM permits alignment of thegreater than ten thousand voxels in the BCAM to be monitored in thestudies time course. The IVOC adds another layer to this system byallowing the user an opportunity for to quantify the optical signal togene expression, cell number, volume or infectious disease burden.

The 2D and 3D systems plugin to existing optical imaging systems. TheBCAM may be specially engineered to work with a multi-animalhigh-throughput holder with integrated anesthesia (2D-holder) and also amirror gantry for multi-orientation images (3D-holder). The 3D-holderpermits simultaneous imaging of the dorsal, ventral, and side views ofthe subject in the BCAM which may be used for the BLT reconstructionalgorithm. The spectral images can be provided to a BLT reconstructionalgorithm that includes an expectation maximization (EM) method and thesimplified spherical harmonics (SP3) equations for modeling in vivolight propagation. Post reconstruction, the system may calculate a totalphoton emission density of a volume of interest (VOI). Using thisresult, the system may identify a cell specific luciferase expressionco-registering it to a novel digital mouse atlas.

This plug-in device for small animal optical imaging systems allows bothhigh-throughput planar image analysis and quantitative 3D data outputwith assignment to specific organs automatically. This platform is aparadigm shifting technology in that it changes the way bioluminescentoptical imaging experiments are analyzed at least because: (i) imageacquisition is operator independent, (ii) reconstruction is automated,(iii) quantification is automated, (iv) data is normalized and (v)reproducible because of a standardized platform. The platform canautomatically analyze optical images to provide unbiased and objectiveinterpretation of the data.

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein can be performed in adifferent sequence, can be added, merged, or left out altogether (e.g.,not all described operations or events are necessary for the practice ofthe algorithm). Moreover, in certain embodiments, operations or eventscan be performed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors or processor cores or onother parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware, or as a combination ofelectronic hardware and executable software. To clearly illustrate thisinterchangeability, various illustrative components, blocks, modules,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware, oras software that runs on hardware, depends upon the particularapplication and design constraints imposed on the overall system. Thedescribed functionality can be implemented in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the disclosure.

Moreover, the various illustrative logical blocks and modules describedin connection with the embodiments disclosed herein can be implementedor performed by a machine, such as an image processing device, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. An image processing device can be or include amicroprocessor, but in the alternative, the image processing device canbe or include a controller, microcontroller, or state machine,combinations of the same, or the like configured to receive, process,and display image data. An image processing device can includeelectrical circuitry configured to process computer-executableinstructions. Although described herein primarily with respect todigital technology, an image processing device may also includeprimarily analog components. For example, some or all of the imagecross-correlation algorithms described herein may be implemented inanalog circuitry or mixed analog and digital circuitry. A computingenvironment can include any type of computer system, including, but notlimited to, a computer system based on a microprocessor, a mainframecomputer, a digital signal processor, a portable computing device, adevice controller, or a computational engine within an appliance, toname a few.

The elements of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein can be embodieddirectly in hardware, in a software module executed by a composite imageprocessing device, or in a combination of the two. A software module canreside in random access memory (RAM) memory, flash memory, read onlymemory (ROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), registers,hard disk, a removable disk, a compact disc read-only memory (CD-ROM),or any other form of a non-transitory computer-readable storage medium.An exemplary storage medium can be coupled to the image processingdevice such that the image processing device can read information from,and write information to, the storage medium. In the alternative, thestorage medium can be integral to the image processing device. The imageprocessing device and the storage medium can reside in an applicationspecific integrated circuit (ASIC). The ASIC can reside in an accessdevice or other image processing device. In the alternative, the imageprocessing device vice and the storage medium can reside as discretecomponents in an access device or other image processing device. In someimplementations, the method may be a computer-implemented methodperformed under the control of a computing device, such as an accessdevice or other image processing device, executing specificcomputer-executable instructions.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, or at least one of Z to each is present.

Unless otherwise explicitly stated, articles such as “a” or “an” shouldgenerally be interpreted to include one or more described items.Accordingly, phrases such as “a device configured to” are intended toinclude one or more recited devices. Such one or more recited devicescan also be collectively configured to carry out the stated recitations.For example, “a structural element configured to carry out recitationsA, B, and C” can include a first structural element configured to carryout recitation A working in conjunction with a second structural elementconfigured to carry out recitations B and C.

As used herein, the terms “determine” or “determining” encompass a widevariety of actions. For example, “determining” may include calculating,computing, processing, deriving, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishing,and the like.

As used herein, the term “selectively” or “selective” may encompass awide variety of actions. For example, a “selective” process may includedetermining one option from multiple options. A “selective” process mayinclude one or more of: dynamically determined inputs, preconfiguredinputs, or user-initiated inputs for making the determination. In someimplementations, an n-input switch may be included to provide selectivefunctionality where n is the number of inputs used to make theselection.

As used herein, the terms “provide” or “providing” encompass a widevariety of actions. For example, “providing” may include storing a valuein a location for subsequent retrieval, transmitting a value directly tothe recipient, transmitting or storing a reference to a value, and thelike. “Providing” may also include encoding, decoding, encrypting,decrypting, validating, verifying, and the like.

As used herein, the term “message” encompasses a wide variety of formatsfor communicating (e.g., transmitting or receiving) information. Amessage may include a machine readable aggregation of information suchas an XML document, fixed field message, comma separated message, or thelike. A message may, in some implementations, include a signal utilizedto transmit one or more representations of the information. Whilerecited in the singular, it will be understood that a message may becomposed, transmitted, stored, received, etc. in multiple parts.

As used herein a “user interface” (also referred to as an interactiveuser interface, a graphical user interface or a UI) may refer to anetwork based interface including data fields and/or other controls forreceiving input signals or providing electronic information and/or forproviding information to the user in response to any received inputsignals. A UI may be implemented in whole or in part using technologiessuch as hyper-text mark-up language (HTML), Flash, Java, .net, webservices, and rich site summary (RSS). In some implementations, a UI maybe included in a stand-alone client (for example, thick client, fatclient) configured to communicate (e.g., send or receive data) inaccordance with one or more of the aspects described.

As used herein, the term “subject” encompasses a wide variety oforganisms. A subject may be a human subject or a non-human subject. Thesubject may be a vertebrate or invertebrate. The subject may refer tothe entire form of the organism or an identifiable portion thereof(e.g., limb, hand, organ, vascular pathway, etc.).

As used herein, the term “data point” generally refers to a single imagetaken from a single subject at a specified time point. The image may bea two dimensional image or a three dimensional image. The image mayinclude pixel and/or voxel data. The image may also include metadata.

As used herein, the term “data set” generally refers to a collection ofdata points. Within a data set, the type of subject, the time point, theprocedure, and/or the therapy can be varied. However, the featuresdescribed allow for cross-correlation of the data points within the dataset.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it can beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As can berecognized, certain embodiments described herein can be embodied withina form that does not provide all of the features and benefits set forthherein, as some features can be used or practiced separately fromothers. The scope of certain embodiments disclosed herein is indicatedby the appended claims rather than by the foregoing description. Allchanges that come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

Various other modifications, adaptations, and alternative designs are ofcourse possible in light of the above teachings. Therefore, it should beunderstood at this time that within the scope of the appended claims thedisclosure may be practiced otherwise than as specifically describedherein. It is contemplated that various combinations or subcombinationsof the specific features and aspects of the embodiments disclosed abovemay be made and still fall within one or more of the inventions.Further, the disclosure herein of any particular feature, aspect,method, property, characteristic, quality, attribute, element, or thelike in connection with an embodiment can be used in all otherembodiments set forth herein. Accordingly, it should be understood thatvarious features and aspects of the disclosed embodiments can becombined with or substituted for one another in order to form varyingmodes of the disclosed inventions. Thus, it is intended that the scopeof the present inventions herein disclosed should not be limited by theparticular disclosed embodiments described above. Moreover, while theinvention is susceptible to various modifications, and alternativeforms, specific examples thereof have been shown in the drawings and areherein described in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various embodiments described and the appended claims.Any methods disclosed herein need not be performed in the order recited.The methods disclosed herein include certain actions taken by apractitioner; however, they can also include any third-party instructionof those actions, either expressly or by implication. The rangesdisclosed herein also encompass any and all overlap, sub-ranges, andcombinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “approximately”, “about”, and“substantially” as used herein include the recited numbers (e.g., about10%=10%), and also represent an amount close to the stated amount thatstill performs a desired function or achieves a desired result. Forexample, the terms “approximately”, “about”, and “substantially” mayrefer to an amount that is within less than 10% of, within less than 5%of, within less than 1% of, within less than 0.1% of, and within lessthan 0.01% of the stated amount.

1-20. (canceled)
 21. A system for providing reproducible imaging resultsindicative of an in vivo experimental result, the imaging results forpresentation via a display unit, the system comprising: an imagereceiver configured to receive first image data and second image data; adata store including a plurality of position definitions, wherein afirst position definition identifies, for an animal subject imaged at afirst time while in a first position, a first location for an anatomicalfeature at the first time, and wherein a second position definitionidentifies, for an animal subject imaged at a second time while in asecond position, a second location for the anatomical feature at thesecond time; a position detector configured to: identify the firstposition definition for processing the first image data based on thefirst image data; and identify the second position definition forprocessing the second image data, based on the second image data; and animage processor configured to: receive a processing protocol identifyinga comparison for image data and an associated result based thereon, thecomparison indicating one or more locations of input image data tocompare and how to compare the indicated input image data, theassociated result indicating an output imaging result to provide for acomparison result; extract a portion of the first image data from thefirst location of the first image data; extract a portion of the secondimage data from the second location of the second image data; generatecomparison data according to the comparison identified in the processingprotocol using image data at the one or more locations identified by thecomparison from the first portion of the first image data and the secondportion of the second image data; generate an imaging result accordingto the processing protocol using the comparison data; and causepresentation of the imaging result via the display unit.
 22. The systemof claim 21, wherein the image receiver is further configured to receivethe first image data from a first sensing device.
 23. The system ofclaim 22, wherein the image receiver is further configured to receivethe second image data from a second sensing device.
 24. The system ofclaim 21, wherein the animal subject imaged at the first time is ananimal test subject, and wherein the animal subject imaged at the secondtime is the animal test subject.
 25. The system of claim 21, wherein:the first location identifies one or more pixel locations for theanatomical feature shown in the first image data, the second locationidentifies one or more pixel locations for the anatomical feature shownin the second image data, and the one or more locations of input imagedata indicated by the comparison comprise one or more pixel locations.26. The system of claim 25, wherein the image processor is furtherconfigured to generate the imaging result by comparing, using theprocessing protocol, first pixel values at the one or more pixellocations of the anatomical feature shown in the first image data withsecond pixel values at the one or more pixel locations of the anatomicalfeature shown in the second image data.
 27. The system of claim 21,wherein the first location identifies one or more voxel locations forthe anatomical feature shown in the first image data, wherein the secondlocation identifies one or more voxel locations for the anatomicalfeature shown in the second image data, and wherein the one or morelocations of input image data indicated by the comparison comprise oneor more voxel locations.
 28. The system of claim 27, wherein the imageprocessor is further configured to generate the imaging result bycomparing, using the processing protocol, first voxel values at the oneor more voxel locations of the anatomical feature shown in the firstimage data with second voxel values at the one or more voxel locationsof the anatomical feature shown in the second image data.
 29. The systemof claim 21, wherein the position detector is further configured toidentify the first position by detecting, within the first image data,an identifiable mark associated with the first position.
 30. The systemof claim 29, wherein the identifiable mark is identified on a mold inwhich the animal subject was placed to capture the first image data, atleast a portion of the mold being shown in the first image data.
 31. Thesystem of claim 21, further comprising an imaging controller configuredto: receive, from an imaging device, information at a third timeidentifying the animal subject to be imaged; identify a third positiondefinition for the animal subject, the third position definitionidentifying, for the animal subject imaged while in a third position, athird location for the anatomical feature; generate a configurationcommand indicating sensor parameters for imaging the animal subjectusing the third position definition and the processing protocol; andtransmit the configuration command to the imaging device.
 32. The systemof claim 31, wherein the imaging controller is further configured togenerate the configuration command using imaging results for the animalsubject stored before the third time.
 33. An image processing systemcomprising: an image receiver configured to receive image data from animaging device; a data store including a plurality of positiondefinitions, wherein each position definition identifies, for a givensubject imaged while in a position, a location for an anatomical featureof the subject; a position detector configured to identify, using theimage data received from the imaging device, the position definition forthe image data; and an image processor configured to generate an imagingresult using a portion of the image data at the location for theanatomical feature identified by the position definition.
 34. The systemof claim 33, wherein: the image data comprises first image data andsecond image data, the plurality of position definitions comprises: afirst position definition that identifies, for a given subject imaged ata first time while in a first position, a first location for ananatomical feature at the first time, and a second position definitionthat identifies, for a given subject imaged at a second time while in asecond position, a second location for the anatomical feature at thesecond time, the first location identifies one or more pixel locationsfor the anatomical feature shown in the first image data, the secondlocation identifies one or more pixel locations for the anatomicalfeature shown in the second image data, and the image processor isfurther configured to generate the imaging result by comparing firstpixel values at the one or more pixel locations of the anatomicalfeature shown in the first image data with second pixel values at theone or more pixel locations of the anatomical feature shown in thesecond image data.
 35. The system of claim 33, wherein: the image datacomprises first image data and second image data, the plurality ofposition definitions comprises: a first position definition thatidentifies, for a given subject imaged at a first time while in a firstposition, a first location for an anatomical feature at the first time,and a second position definition that identifies, for a given subjectimaged at a second time while in a second position, a second locationfor the anatomical feature at the second time, the first locationidentifies one or more voxel locations for the anatomical feature shownin the first image data, the second location identifies one or morevoxel locations for the anatomical feature shown in the second imagedata, and the image processor is further configured to generate theimaging result by comparing, using the processing protocol, first voxelvalues at the one or more voxel locations of the anatomical featureshown in the first image data with second voxel values at the one ormore voxel locations of the anatomical feature shown in the second imagedata.
 36. The system of claim 33, wherein the position detector isfurther configured to identify the position by detecting, within theimage data, an identifiable mark associated with the position.
 37. Thesystem of claim 36, wherein the identifiable mark is identified on apositioning assembly in which the given subject was placed to capturethe image data, at least a portion of the positioning assembly beingshown in the image data.
 38. The system of claim 33, wherein the imagedata comprises non-optical image data.
 39. The system of claim 33,wherein the imaging device is configured to capture image data of thegiven subject while the given subject is positioned within an opticallytransparent animal mold.
 40. A computer implemented method comprising:receiving a plurality of position definitions, wherein a positiondefinition identifies, for a given subject imaged while in a position, alocation for an anatomical feature of the subject; receiving image datafrom an imaging device; identifying, using the image data received fromthe imaging device, the position definition for the image data; andgenerating an imaging result using a portion of the image data at thelocation for the anatomical feature identified by the positiondefinition.